Financial industrial agglomeration refers to a special industrial spatial structure in which headquarters-based institutions, including national financial regulatory authorities, financial intermediaries, multinational financial enterprises, and domestic financial firms, concentrate in specific regions and maintain close operational connections with international institutions, multinational corporations, and the headquarters of large domestic enterprises (360 Encyclopedia). Under the background of economic and financial globalization, the spatial agglomeration of the financial industry has become increasingly prominent. The interactive development between the financial industry and the real economy has fostered the formation of financial industrial clusters, which accelerate financial innovation, reduce transaction costs, and further drive regional economic growth through agglomeration effects ([33]).

Regarding the economic effects, spatial spillover, and micro-impact of financial agglomeration, rich research results have been formed at home and abroad: [34] used a spatial panel model of 285 prefecture-level cities in China from 2008 to 2021 to confirm that financial agglomeration has an inverted U-shaped direct effect and positive spatial spillover on urban economic resilience, and the agglomeration pattern shows the characteristics of evolving from point-like to block-like agglomeration ([34]); [31] conducted research from the perspective of enterprise sustainable development and found that financial agglomeration can significantly improve the comprehensive ESG performance of enterprises by optimizing resource allocation and upgrading industrial structure. The effect has property rights and regional heterogeneity; [30] used provincial panel data to verify that financial agglomeration has a positive promoting effect on regional financial resilience, but the central and western regions are constrained by the siphon effect and cannot enjoy the agglomeration dividend. [20] focused on 46 prefecture-level cities in Southwest China, confirming that there is a significant spatial correlation in financial agglomeration, and its spillover effect can effectively promote regional urbanization; [21] used Guangxi as a research sample and concluded, through location entropy calculation, that the overall level of financial agglomeration in the province is low and the regional resource distribution is unbalanced, and proposed targeted paths for improving the quality and efficiency of finance. Since Guizhou began implementing the “Attracting Finance to Guizhou” strategy in 2012, and in the past two years, the government has focused on building the Guizhou Financial City project. Guizhou’s financial industry has gradually entered a golden age, and the gross domestic product of the financial industry has increased year by year. The 2019 Guizhou Provincial Government Work Report emphasized that in order to fully promote high-quality economic development, it is necessary to “strengthen the construction of the local financial system, enhance the linkage between government, finance, and enterprises, strengthen financial product innovation, and focus on alleviating the difficulties and high costs of financing for small and micro enterprises, private enterprises, and agriculture. Increase the cultivation and support for enterprise listing and actively develop regional equity markets.” Therefore, accelerating the agglomeration and development of Guizhou’s financial industry and giving full play to the agglomeration effect of finance is of great practical significance for Guizhou to accelerate the transformation and upgrading of its industrial economy and fully promote high-quality economic development.

Scientifically evaluating the level of financial agglomeration is a fundamental and crucial issue for accelerating the agglomeration and development of the financial industry. Currently, commonly used methods for evaluating the level of financial agglomeration at home and abroad include ([26]): Evaluation methods based on regional financial agglomeration level (such as industry concentration, Herfindahl index, location Gini coefficient, industrial cluster index, Halley-White index, etc.), and distance-based evaluation methods (such as K-function, L-function, and SP index). In addition, to cover more information, scholars have also introduced multi-attribute decision-making methods into the evaluation of financial agglomeration level. 1) In terms of the indicator system, the traditional financial agglomeration evaluation framework is mostly limited to the traditional financial industry dimension. [5] constructed an evaluation indicator system for the degree of financial agglomeration in China from four aspects: the overall scale of finance, banking industry, securities industry and insurance industry ([5]). [25] constructed a measurement index system for the level of financial agglomeration in central cities from four aspects: financial background, financial scale, financial density, and financial depth. [14] constructed a regional financial agglomeration level measurement index system, which includes primary indicators such as the insurance market, credit market, trust market, and securities market. [4] constructed a regional financial agglomeration level evaluation index system with financial scale, financial institutions, and financial talents as primary indicators ([4]). [7] established a Shandong financial agglomeration degree evaluation index system, which includes primary indicators such as financial resources, financial institutions, and financial output. [16] established a comprehensive evaluation index system for Guangxi financial agglomeration that includes economic aggregate and financial scale. With the rapid popularization of digital financial business models, digital inclusive finance has become an indispensable core dimension for regional economic and financial evaluation. [8] empirically found using panel data from 16 cities and prefectures in Yunnan that digital inclusive finance can significantly increase the income of rural residents, and the income increase effect has regional heterogeneity and nonlinear threshold characteristics; [17] confirmed based on national provincial panel data that digital inclusive finance can narrow the urban-rural income gap through two paths: industrial upgrading and non-agricultural employment, with significant differences in threshold effects in the sub-dimensions of coverage breadth and usage depth; [27] and [28] further demonstrated the key value of digital inclusive finance in narrowing the urban-rural income gap, promoting urban-rural integration, and helping common prosperity, providing sufficient theoretical and empirical support for this paper to add the dimension of digital inclusive finance in addition to traditional financial industry indicators. 2) In terms of evaluation methods, the mainstream measurement tools in existing research include principal component analysis ([5]) Factor analysis method 458 ([14]; [16]; [25]) Vertical and horizontal grading method ([4]), and entropy weight TOPSIS method ([7]). The Entropy Weight-TOPSIS model, relying on its advantages of objective weighting, multi-dimensional hierarchical ranking, and dynamic time-series evaluation, has been widely used in various quantitative studies on regional development levels. [6] built a three-dimensional indicator system and used the model to measure the development level of Xinjiang’s land ports, finding that international logistics channels are the core factor restricting the upgrading of land ports ([6]). [10] used the Entropy Weight-TOPSIS method to measure the logistics development level of China-ASEAN countries, confirming the serious imbalance in regional development and proposing differentiated development policies for groups. [11] used the same model to evaluate the standardized development of provinces across the country and identified a pyramid hierarchy pattern of leading in the east and lagging in the west. To sum up, the existing financial agglomeration level evaluation indicator systems have their own characteristics, but they all ignore the use of Internet big data, resulting in insufficient information; In addition, existing methods for evaluating the level of financial agglomeration have their own advantages, but they all lack ambiguity and hesitation, making it difficult to reflect the ambiguity and hesitation of decision-makers in evaluating objective objects.

Baidu Index is a data sharing platform based on Baidu’s massive user behavior data. It is one of the most important statistical analysis platforms in the current Internet and even in the entire data era. Since its release in 2006, it has become an important basis for marketing decisions and evaluations for many companies. Through the Baidu Index function module, people can study keyword search trends, understand changes in user demand, monitor media sentiment trends, and locate digital consumer characteristics; they can also analyze market characteristics from an industry perspective (http://index.baidu.com). A large number of existing empirical studies have verified the feasibility and scientific nature of using Baidu Index to quantify public attention and capture market demand: [23] used Baidu Index as an input variable and combined it with various machine learning models to complete the prediction of tourist volume in scenic spots, proving that there is a close nonlinear correlation between search index and actual economic activities, and there is also a spatial distance attenuation spillover effect; [2] based on ten years of Baidu Index data systematically depicted the spatiotemporal differentiation of traditional famous flowers’ online attention, confirming that the level of regional economic development, industrial distribution, and seasonal cycle all significantly affect the public search popularity ([2]). Chen Zhiyuan et al. empirically found that Baidu Index, as a proxy variable for investor attention, is significantly positively correlated with stock market performance and can improve the accuracy of stock prediction models ([3]). Jiang Wenjie et al. studied and showed that there is a cointegration relationship between Baidu Index and real estate prices, and prediction models incorporating search indexes have higher fitting degrees and advanced predictive capabilities ([12]). Meng Xuejing et al. combined text mining and Baidu Index to screen keywords and construct an investor sentiment index with leading characteristics, providing a new tool for capital market research ([22]). Liu Jiayi et al. revealed through Baidu Index that there are significant regional and city size differences in tourism public opinion attention, driven by factors such as population, income, and informatization level ([18]). Xiong Lifang et al. used Baidu Index to simulate urban information flow, depict the spatiotemporal evolution characteristics of the Yangtze River Delta city network, and found that the regional network structure is continuously optimized and internal connections are continuously strengthened ([32]). Currently, the Baidu Index module has been widely used in research on stock market performance, investor sentiment, tourism public opinion, real estate prices, and urban network characteristics. This also provides a reliable reference for this paper to introduce Baidu search indexes for the financial industry, banking industry, securities industry, and insurance industry to measure public and government attention to finance.

In the realm of hesitant fuzzy decision-making research, scholars have continuously optimized models and algorithms centered around the expression and processing of fuzzy uncertainty information. Hu Guanzhong et al. constructed formulas for interval hesitant fuzzy entropy and cross entropy, established a multi-attribute group decision-making method under the condition of unknown attribute weights, and improved the theoretical framework of interval hesitant fuzzy decision-making ([9]). Addressing the shortcomings of the traditional TOPSIS method, Li Chaoqun et al. proposed the hesitant fuzzy vertical distance combined with the orthogonal projection method to optimize the ranking logic of schemes ([13]); Liu Jun introduced the Maclaurin symmetric mean operator to construct a group decision-making model, which can adjust parameters based on decision-makers’ preferences to enhance model adaptability ([18]); Wei and Ge proposed the hesitant fuzzy linguistic power mean operator to solve the evaluation fusion problem with unknown expert weights ([29]). Based on this, combined models such as hesitant fuzzy linguistic PROMETHEE and DEMATEL-VIKOR have been applied to scenarios such as Sichuan wine brand evaluation, military training assessment, cloud computing service provider selection, local higher education evaluation, and urban-type water disaster risk assessment, confirming the effectiveness of such methods in handling qualitative fuzzy information.

In the practice of multi-attribute comprehensive evaluation, the single subjective weighting method is easily affected by human subjective preferences, and the traditional objective weighting model is difficult to take into account the differences in indicator information and the hierarchical ranking requirements of the schemes, making it difficult to achieve an accurate quantitative assessment of regional development levels. To this end, the entropy weight-TOPSIS fusion model has been proposed and continuously improved by the academic community: the entropy weight method relies on the information entropy of the indicators to achieve purely objective weighting and avoid human scoring bias; the TOPSIS method completes the hierarchical ranking by measuring the Euclidean distance and closeness between the evaluation object and the positive and negative ideal solutions. The combination of the two can fully explore all the information contained in the multi-dimensional indicators. At present, this combined model has been widely used in the field of quantitative measurement of regional development. [6] used it for dynamic evaluation of the development level of Xinjiang land ports. [10] used this model to calculate the gradient difference in logistics development between China and ASEAN countries. [11] completed the national provincial standardized development echelon division based on entropy weight-TOPSIS, which fully verified the scientificity and applicability of the method in multi-dimensional regional comprehensive evaluation. Brans and Vincke were the first to propose the partial order ranking organization method-PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), which compares alternative solutions in partial order by constructing preference functions among criteria. It provides a systematic framework for handling multi-attribute and multi-objective ranking problems and has become one of the most widely used methods in the field of multi-criteria decision-making ([1]). However, traditional methods often require decision-makers to express their preferences as precise numerical values, making it difficult to capture the uncertainty that commonly exists in real-world decision-making. To overcome this limitation, Rodríguez et al. introduced the concept of Heterogeneous Fuzzy Linguistic Term Sets (HFLTS), allowing decision-makers to hesitate between multiple linguistic terms. This combines the expressive advantage of linguistic variables with the fuzzy characterization ability of hesitant fuzzy sets, providing a new mathematical tool for handling decision-making hesitancy and uncertainty ([24]). Based on this, Liao et al. defined distance measures and similarity measures for HFLTS, systematically studied their mathematical properties, and applied them to multi-criteria decision-making problems, thereby improving the theoretical system of hesitant fuzzy linguistic decision-making ([15]).

In view of this, this paper addresses the shortcomings of the existing financial agglomeration evaluation system, which has the lack of big data indicators and insufficient information coverage. It introduces Baidu search index to characterize the public and government’s financial attention, adds the evaluation dimension of digital inclusive finance, and builds a multi-level secondary indicator evaluation system around the four major sectors of financial industry, banking industry, securities industry and insurance industry. It selects panel data of nine cities and prefectures in Guizhou from 2011 to 2025, uses the entropy weight-TOPSIS method to quantitatively measure the comprehensive level of financial agglomeration in various places, analyzes the gap in financial agglomeration development in the province from the two dimensions of temporal evolution and spatial differentiation, and proposes targeted policy recommendations to promote the quality and efficiency of financial agglomeration in Guizhou based on the current situation of regional financial development.

This study is based on the core connotation of financial agglomeration, sorts out and integrates the conclusions of existing relevant research at home and abroad, draws on the construction logic of the China Financial Center Index (CFCI) released by the China (Shenzhen) Development Institute, and combines the actual development characteristics of the financial industry in various prefecture-level cities in Guizhou Province. Following the selection criteria of scientificity, typicality, objectivity and neutrality, regional adaptability, horizontal comparability, and practical feasibility, the article incorporates the Baidu search index to reflect the market and government’s attention to finance. Around five dimensions-financial industry, banking industry, securities industry, insurance industry, and digital inclusive finance - a comprehensive evaluation framework for the level of financial agglomeration in prefecture-level cities in Guizhou Province, covering 30 secondary indicators, is constructed. The specific indicator composition is detailed in Table 1.

Table 1.Evaluation index system for financial agglomeration level in Guizhou prefecture-level cities.

Indicator Explanation:

1) Location entropy, also known as specialization rate, indicates a higher level of industrial agglomeration in the region. Generally, when the location entropy is greater than 1, the region has an advantage in a certain economic indicator nationwide; when the location entropy is less than 1, it is at a disadvantage. The calculation formula is L Q i j = ( q i j / q j ) / ( q i / q ) , where q i j represents j the total output value of the region’s i industry, q j represents j the total output value of all industries in the region; q i represents the national i industry, q Represents the total output value of all industries nationwide. In Table 1, the location entropy of the financial industry is calculated as follows: (Gross GDP of the financial industry in prefecture-level cities/GDP of prefecture-level cities)/(Gross GDP of the financial industry in Guizhou/GDP of Guizhou); the location entropy of the banking industry is calculated as follows: (Year-end deposit balance of financial institutions in prefecture-level cities/Gross GDP of the financial industry in prefecture-level cities)/(Year-end deposit balance of financial institutions in Guizhou/Gross GDP of the financial industry in Guizhou); the location entropy of the securities industry is calculated as follows: (Total market value of stocks in prefecture-level cities/Gross GDP of the financial industry in prefecture-level cities)/(Total market value of stocks in Guizhou/Gross GDP of the financial industry in Guizhou); the location entropy of the insurance industry is calculated as follows: (Annual premium income of insurance in prefecture-level cities/Gross GDP of the financial industry in prefecture-level cities)/(Annual premium income of insurance in Guizhou/Gross GDP of the financial industry in Guizhou).

2) Baidu Search Index quantifies the weighted sum of keyword search frequencies on Baidu’s web search platform, using netizens’ search volumes as the data foundation. The index platform restricts the geographic scope to prefecture-level cities in Guizhou Province, incorporating both PC and mobile search traffic, and directly adopts the integrated composite index output by the platform. For data processing, the daily indices are averaged by calendar year to generate annual public attention values for each city. All raw Baidu Index data were batch-exported on July 12, 2026. Missing daily observations within a year are filled via linear interpolation; if a city-year’s complete data is unavailable, the sample is assigned a value of 0 following established literature practices. In Table 1, the public attention index for the financial sector is measured by the comprehensive Baidu search index (including PC and mobile trends) using “finance” as the keyword, reflecting the influence of the financial industry in a given region. The public attention index for the banking sector is similarly measured using “bank”, indicating the influence of the banking industry in a region. The public attention index for the securities sector is derived from searches for “securities”, showcasing the impact of the securities industry. Lastly, the public attention index for the insurance sector is calculated based on searches for “insurance”, representing the influence of the insurance industry in a specific area.

3) Government Attention Index refers to the frequency of keywords related to financial sub-sectors mentioned in the government work reports of prefecture-level cities, serving as a metric to evaluate local governments’ policy emphasis, support orientation, and developmental planning for specific financial industries. The corpus for this indicator comprises the full texts of government work reports from nine cities/prefectures in Guizhou Province spanning 2011-2025. In the statistical process, repeated keywords within the same paragraph are counted individually, total keyword frequency is calculated on a per-report basis rather than at the paragraph level, and manual screening is performed to exclude rhetorical expressions and non-financial allusive references, retaining only financially-relevant content concerning local industrial development, policy support, or practical deployment. In Table 1, the government attention indices for finance, banking, securities, and insurance are derived from searches for the keywords “finance”, “bank”, “securities”, “insurance” within the corresponding municipal government reports.

4) The four indicators related to digital inclusive finance selected in this paper are all taken from the prefecture-level city digital inclusive finance index system released by the Digital Finance Research Center of Peking University. The data and complete compilation rules can be viewed and obtained through the official website of the Digital Finance Research Center of Peking University (https://idf.pku.edu.cn/). Among them, the Digital Inclusive Finance Overall Index comprehensively reflects the overall development level of digital finance in various regions; the Digital Finance Coverage Breadth Index measures the popularity of digital financial accounts and the scale of user reach in the region; the Digital Finance Usage Depth Index depicts the actual usage activity of diversified digital financial businesses such as payment, credit, insurance, and wealth management; and the Inclusive Finance Digitalization Degree Index represents the mobile convenience, low-cost advantages, and big data credit application level of digital financial services. These four indicators together depict the complete level of regional digital finance development, making up for the lack of digital business format dimension in traditional financial agglomeration indicators.

Traditional financial agglomeration evaluations often suffer from limitations such as single data sources, insufficient dimensional coverage, and neglect of market and public feedback. This paper integrates multi-source data, including statistical data, financial business data, the digital inclusive finance index, Baidu attention, and government attention, to construct a comprehensive evaluation system comprising 30 secondary indicators across five major sectors: finance, banking, securities, insurance, and digital inclusive finance. Based on panel data from nine prefecture-level cities and prefectures in Guizhou Province from 2011 to 2025, this paper adopts the entropy-weight-TOPSIS model to conduct multi-dimensional and refined quantitative measurement, revealing the evolutionary patterns of regional financial agglomeration from both temporal and spatial perspectives. Specific conclusions are as follows:

1) Time-Series Dimension. According to the multi-source data calculation results, the overall financial agglomeration level of nine prefecture-level cities and prefectures in Guizhou shows a fluctuating upward trend from 2011 to 2025, and the growth differences among prefecture-level units are obvious. Zunyi City ranks first in growth, with a net increase of 0.2006 over 15 years and realizes remarkable phased improvement. Tongren City and Qiannan Prefecture follow with good growth momentum. By contrast, Liupanshui City shows almost stagnant growth with a tiny net increase. Although Guiyang City does not have the highest growth rate, its comprehensive score rises from 0.4579 in 2011 to 0.5122 in 2025, always ranking first in the province and possessing solid core advantages supported by multi-dimensional financial resources. Bijie City and Qiandongnan Prefecture show obvious short-term peaks in individual years (Bijie in 2018, Qiandongnan in 2017), but their long-term growth is limited. Each prefecture-level unit presents phased fluctuations in its development process. Nevertheless, the overall baseline of provincial financial agglomeration rises year by year, and the comprehensive quality and coordination of regional financial development are continuously improved.

2) Spatial Dimension. Based on the TOPSIS scores calculated from multi-source data, this paper conducts spatial hierarchical analysis for 2011, 2018 and 2025 with differentiated grading thresholds. Map colors from light blue to dark blue represent increasing financial agglomeration scores. In 2011, Guizhou presents an obvious single-core polarization pattern dominated by Guiyang; Zunyi also enters the highest-value tier, while most eastern and southern prefecture-level units are in low-value agglomeration areas, and the east-west development gap is prominent. In 2018, the spatial structure underwent phased readjustment. Guiyang and Bijie City form high-value regions, whereas Zunyi drops to the medium-high tier. A large number of low-value agglomeration units are still widely distributed in eastern and southern Guizhou, and the radiation-driving effect of central Guizhou has not been fully exerted. In 2025, Guiyang and Zunyi jointly constitute dual high-value poles. However, some prefecture-level units including Tongren City, Liupanshui City and Qianxinan Prefecture still stay at relatively low agglomeration levels; low-value agglomeration units have not completely disappeared. In general, Guizhou’s financial agglomeration pattern gradually evolves toward multi-polar collaborative development, but hierarchical differences among prefecture-level units remain objectively existing.

On the basis of spatiotemporal characteristics and industrial shortcomings identified through multi-source data quantification, this paper proposes differentiated optimization paths to facilitate balanced and high-quality development of financial agglomeration in Guizhou Province.

First, promote a tiered layout according to regional resource endowment differences. Give full play to the radiation-driving function of Guiyang and Zunyi as dual core growth poles, break factor-flow barriers, and advance the cross-prefectural optimal allocation of financial resources. For Tongren City, Qianxinan Prefecture and Liupanshui City which remain at low agglomeration levels, local governments should further improve financial infrastructure, build systems for financial talent introduction and training, and gradually narrow hierarchical gaps in financial agglomeration.

Second, implement targeted governance aiming at industrial structural weaknesses reflected by the evaluation index system. Bijie City and Tongren City suffer from insufficient vitality in the capital market; hence, it is necessary to foster local high-quality enterprises to promote listing work. Anshun City and Tongren City face insufficient vitality in the banking sector, so it is necessary to expand local financial entities and iterate inclusive credit product supply. In view of the underdeveloped insurance industry in Bijie City and Anshun City, insurance services should be extended to grassroots jurisdictions to tap the market potential of county-level insurance business.

Third, explore the practical application value of multi-source data. Integrate sub-dimensions of digital inclusive finance as well as public- and government-oriented financial attention indicators. Considering both realistic development conditions and market demand, authorities can flexibly adjust financial industry support orientations. Meanwhile, consolidate the development foundation of traditional financial institutions, release the driving potential of digital finance, and enhance the overall strength of provincial-wide financial agglomeration.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

This research was funded by the Regional Project of National Natural Science Foundation of China, grant number 71861003.

Most of the data generated or analyzed during this study are included in the manuscript, and the rest data are promptly available to readers without undue qualifications.

Conceptualization, Mu Zhang; methodology, Mu Zhang; validation, Mu Zhang and Ya Zhang; formal analysis, Mu Zhang and Ya Zhang; investigation, Ya Zhang; resources, Mu Zhang; data curation, Mu Zhang and Ya Zhang; writing—original draft preparation, Mu Zhang and Ya Zhang; writing—review and editing, Mu Zhang and Ya Zhang; supervision, Mu Zhang; project administration, Mu Zhang; funding acquisition, Mu Zhang. All authors have read and agreed to the published version of the manuscript.