Institutional research hotspot tracking and visual analysis method and system

Abstract

The present application belongs to the technical field of visualization, and provides a method and system for tracking and visualizing scientific research hotspots, which comprises the following steps: obtaining scientific research literature data in multiple dimensions; performing cluster analysis and heat mining on the scientific research literature data to obtain target scientific research literature data with potential hotspots; tracking and analyzing the heat persistence and heat stability of the target scientific research literature data, and screening to obtain hot scientific research literature data; and performing visual analysis on the hot scientific research literature data to obtain hot visual analysis results. The present application can improve the accuracy of obtaining scientific research hotspots and the visual effect by obtaining scientific research literature data in multiple dimensions, tracking and analyzing the heat persistence and heat stability of target scientific research literature data with potential hotspots, screening to obtain hot scientific research literature data, and finally performing visual analysis.

Description

Technical Field

[0001] This invention relates to the field of visualization technology, and in particular to a method and system for tracking and visualizing research hotspots in institutions. Background Technology

[0002] With the widespread application of computer networks and information technology, researchers have made it an important routine task to study and pay attention to research hotspots in order to improve the quality and efficiency of their research. However, the discovery and tracking of research hotspots is not an easy task when faced with a massive amount of research literature.

[0003] Visual analytics is a product of the development of information visualization and scientific visualization. It is an effective means and approach for people to understand and interpret large-scale complex situations. Through visualization algorithms, it realizes graphical visualization models that can be used to display multi-dimensional or high-dimensional data. This method can be applied to the analysis and tracking of scientific research hotspots.

[0004] Topic modeling-based literature hotspot analysis is an important method for exploring the state of research in a specific field. It involves analyzing published academic literature or patents in that field, with academic literature serving as a significant indicator of research development. Current literature analysis utilizes topic modeling and visualization to display multi-scale information about the topic model. However, given the rapid pace of technological advancements, the constant emergence of new research hotspots, and the varying research capabilities of different research institutions, these hotspots are characterized by rapid changes and difficulty in tracking.

[0005] Therefore, it is necessary to provide methods and systems for tracking and visualizing research hotspots in institutions. Summary of the Invention

[0006] This invention provides a method and system for tracking and visualizing research hotspots in institutions. By acquiring research literature data from multiple dimensions and tracking and analyzing the sustainability and stability of the popularity of target research literature data with potential hotspots, hotspot research literature data is obtained through screening. Finally, visualization analysis is performed, which can improve the accuracy of research hotspot acquisition and the visualization effect.

[0007] This invention provides a method for tracking and visualizing research hotspots in institutions, including:

[0008] Acquire scientific literature data from multiple dimensions;

[0009] Cluster analysis and popularity mining are performed on scientific research literature data to obtain target scientific research literature data with potential hot spots;

[0010] We track and analyze the persistence and stability of the popularity of target scientific research literature data, and then select and obtain hot scientific research literature data.

[0011] Visual analysis of popular scientific literature data is performed to obtain the results of popular science visualization analysis.

[0012] Furthermore, scientific research literature data is obtained from multiple dimensions, including:

[0013] Based on the constructed data acquisition tools, scientific literature data is obtained from multiple dimensions, including but not limited to research institutions, research teams, research fields, and geographical regions.

[0014] Furthermore, cluster analysis and popularity mining are performed on the scientific research literature data to obtain target scientific research literature data with potential hot topics, including:

[0015] Cluster the scientific literature data to obtain research direction category group data and the topic category group data to which the research direction category group belongs, forming several data pairs;

[0016] By using heat mining analysis tools, heat analysis and judgment are performed on data pairs to obtain target scientific literature data with potential hot spots.

[0017] Furthermore, using heat mining analysis tools, heat analysis and judgment are performed on data pairs to obtain target scientific research literature data with potential hotspots, including:

[0018] Using data mining tools, we can mine the data attention of the data pairs to obtain the first set of data pairs; data mining tools include, but are not limited to, tools involving data statistics, data retrieval, machine learning, expert systems, and pattern recognition.

[0019] The first set of data pairs is screened for data credibility to obtain a second set of data pairs with credibility greater than the set credibility threshold. The second set of data pairs is then used as target scientific literature data with potential hot topics.

[0020] Furthermore, the target scientific research literature data is tracked and analyzed for its persistence and stability of popularity, and hot scientific research literature data is obtained through screening, including:

[0021] Based on the ChatGpt model analysis tool, according to the set analysis tasks, intelligent analysis of the popularity persistence of target scientific literature data is carried out to obtain the popularity persistence analysis results.

[0022] Based on the hot topic trend prediction and analysis tool, intelligent analysis of the heat stability of target scientific research literature data is performed to obtain the heat stability analysis results.

[0023] Based on the results of the heat persistence analysis and the heat stability analysis, hot scientific literature data were selected.

[0024] Furthermore, based on the ChatGpt model analysis tool, according to the set analysis tasks, intelligent analysis of the persistence of popularity is performed on the target scientific research literature data to obtain the results of the persistence of popularity analysis, including:

[0025] Obtain the ChatGpt model and set the analysis task for the ChatGpt model; the analysis task is to construct a question task involving the popularity of the target scientific literature data based on several set question templates.

[0026] Based on the ChatGpt model, intelligent analysis of the persistence of popularity of target scientific literature data is performed according to the analysis task, and several analysis results are obtained. The analysis results include the popularity persistence period, the probability of popularity decline, the probability of popularity rise, the probability of popularity being impacted, and the probability of popularity being cyclical.

[0027] Furthermore, based on hot topic trend prediction and analysis tools, intelligent analysis of the heat stability of target scientific research literature data is performed to obtain heat stability analysis results, including:

[0028] A hotspot trend prediction and analysis tool based on convolutional neural networks;

[0029] Based on the hot topic trend prediction and analysis tool, the trend change of the popularity of target scientific research literature data is predicted, and the prediction results are obtained, including the popularity trend change curve.

[0030] The heat trend curve is fitted with the set heat stability trend curve to obtain the curve fitting degree, and the curve fitting degree is used as the result of the heat stability analysis.

[0031] Furthermore, based on the results of the popularity persistence analysis and popularity stability analysis, hot research literature data was obtained, including:

[0032] Based on the results of the heat persistence analysis, the first set of target scientific literature data that meets the first set conditions is selected and obtained. The first set conditions are: the heat persistence period is greater than the set persistence period threshold, the probability of heat decrease is less than the set first probability threshold, the probability of heat increase is less than the set second probability threshold, the probability of heat being impacted is less than the set third probability threshold, and the probability of heat being cyclical is greater than the set fourth probability threshold.

[0033] Based on the results of the heat persistence analysis, a second set of target scientific literature data that meets the second set of conditions is selected; the second set of conditions is: the curve fit degree is greater than the set fit degree threshold.

[0034] Data that simultaneously belongs to both the first and second group of target research literature data is considered as hot research literature data.

[0035] Furthermore, visual analysis is performed on the data of popular scientific research literature to obtain the results of the popularity visualization analysis, including:

[0036] Obtain visualization and analysis tools;

[0037] Visualization analysis tools are used to perform visualization analysis on hot research literature data to obtain hotspot visualization analysis results.

[0038] An institutional research hotspot tracking and visualization analysis system, including:

[0039] The data acquisition module is used to acquire scientific literature data from multiple dimensions;

[0040] The target scientific literature data acquisition module is used to perform cluster analysis and popularity mining on scientific literature data to obtain target scientific literature data with potential hot spots.

[0041] Hot research literature data is used to track and analyze the continuity and stability of the popularity of target research literature data, and to filter and obtain hot research literature data.

[0042] The Hotspot Visualization Analysis module is used to perform visual analysis on hot research literature data and obtain hotspot visualization analysis results.

[0043] Compared with existing technologies, this invention has the following advantages and beneficial effects: by acquiring scientific research literature data from multiple dimensions, and by tracking and analyzing the sustainability and stability of the popularity of target scientific research literature data with potential hot spots, hot scientific research literature data can be screened and obtained. Finally, visualization analysis can be performed, which can improve the accuracy of scientific research hot spot acquisition and the visualization effect.

[0044] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0045] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0047] Figure 1 A schematic diagram illustrating the steps of methods for tracking and visualizing research hotspots in an institution;

[0048] Figure 2A schematic diagram illustrating the steps involved in obtaining target research literature data with potential hot topics;

[0049] Figure 3 This is a schematic diagram of the structure of an institutional research hotspot tracking and visualization analysis system. Detailed Implementation

[0050] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0051] This invention provides a method for tracking and visualizing research hotspots in institutions, such as... Figure 1 As shown, it includes:

[0052] Acquire scientific literature data from multiple dimensions;

[0053] Cluster analysis and popularity mining are performed on scientific research literature data to obtain target scientific research literature data with potential hot spots;

[0054] We track and analyze the persistence and stability of the popularity of target scientific research literature data, and then select and obtain hot scientific research literature data.

[0055] Visual analysis of popular scientific literature data is performed to obtain the results of popular science visualization analysis.

[0056] The working principle of the above technical solution is as follows: In order to realize the method of tracking and visualizing the research hotspots of institutions, the method proposed in this invention is to first acquire multi-dimensional research literature data; then perform cluster analysis and heat mining on the research literature data to obtain target research literature data with potential hotspots; then perform heat persistence and heat stability tracking analysis on the target research literature data, and screen to obtain hot research literature data; finally, perform visual analysis on the hot research literature data to obtain the hotspot visualization analysis results.

[0057] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, scientific research literature data is acquired from multiple dimensions, and the heat continuity and heat stability of target scientific research literature data with potential hot spots are tracked and analyzed to obtain hot scientific research literature data. Finally, visualization analysis is performed, which can improve the accuracy of scientific research hot spot acquisition and visualization effect.

[0058] In one embodiment, scientific literature data is acquired from multiple dimensions, including:

[0059] Based on the constructed data acquisition tools, scientific literature data is obtained from multiple dimensions, including but not limited to research institutions, research teams, research fields, and geographical regions.

[0060] The working principle of the above technical solution is as follows: In order to achieve multi-dimensional acquisition of scientific literature data, this invention acquires scientific literature data from multiple dimensions based on the constructed data acquisition tool; the multiple dimensions include, but are not limited to, scientific research institutions, research teams, research fields and their geographical locations.

[0061] The beneficial effects of the above technical solution are as follows: by adopting the solution provided in this embodiment, the accuracy and comprehensiveness of scientific literature data can be guaranteed through acquisition from multiple dimensions.

[0062] In one embodiment, such as Figure 2 As shown, cluster analysis and popularity mining are performed on scientific research literature data to obtain target scientific research literature data with potential hot topics, including:

[0063] Cluster the scientific literature data to obtain research direction category group data and the topic category group data to which the research direction category group belongs, forming several data pairs;

[0064] By using heat mining analysis tools, heat analysis and judgment are performed on data pairs to obtain target scientific literature data with potential hot spots.

[0065] The working principle of the above technical solution is as follows: In order to achieve cluster analysis and heat mining of scientific research literature data to obtain target scientific research literature data with potential hot spots, this invention first clusters the scientific research literature data to obtain research direction category group data and the topic category group data to which the research direction category group belongs, forming several group data pairs; then, using heat mining analysis tools, the heat analysis and judgment of the group data pairs are performed to obtain target scientific research literature data with potential hot spots.

[0066] The beneficial effects of the above technical solution are as follows: by using the solution provided in this embodiment, the scientific research literature data is first clustered, and then the heat mining analysis tool is used to perform heat analysis and judgment on the data pairs, which can ensure that accurate target scientific research literature data with potential hot spots are obtained.

[0067] In one embodiment, a heat mining analysis tool is used to perform heat analysis and judgment on data pairs to obtain target scientific literature data with potential hotspots, including:

[0068] Using data mining tools, we can mine the data attention of the data pairs to obtain the first set of data pairs; data mining tools include, but are not limited to, tools involving data statistics, data retrieval, machine learning, expert systems, and pattern recognition.

[0069] The first set of data pairs is screened for data credibility to obtain a second set of data pairs with credibility greater than the set credibility threshold. The second set of data pairs is then used as target scientific literature data with potential hot topics.

[0070] The working principle of the above technical solution is as follows: In order to use heat mining analysis tools to perform heat analysis and judgment on data pairs and obtain target scientific research literature data with potential hot spots, this invention first uses data mining tools to mine the data attention of data pairs to obtain a first set of data pairs; data mining tools include, but are not limited to, tools involving data statistics, data retrieval, machine learning, expert systems and pattern recognition; then, the first set of data pairs is screened for data credibility to obtain a second set of data pairs with credibility greater than a set credibility threshold, and the second set of data pairs is regarded as target scientific research literature data with potential hot spots.

[0071] The beneficial effects of the above technical solution are as follows: by using the solution provided in this embodiment, and by utilizing the heat mining analysis tool, heat analysis and judgment can be performed on the data pairs to obtain accurate target scientific literature data with potential hot spots.

[0072] In one embodiment, the target scientific research literature data is tracked and analyzed for its persistence and stability of popularity, and hot scientific research literature data is obtained by filtering, including:

[0073] Based on the ChatGpt model analysis tool, according to the set analysis tasks, intelligent analysis of the popularity persistence of target scientific literature data is carried out to obtain the popularity persistence analysis results.

[0074] Based on the hot topic trend prediction and analysis tool, intelligent analysis of the heat stability of target scientific research literature data is performed to obtain the heat stability analysis results.

[0075] Based on the results of the heat persistence analysis and the heat stability analysis, hot scientific literature data were selected.

[0076] The working principle of the above technical solution is as follows: In order to track and analyze the persistence and stability of the popularity of target scientific research literature data and to screen and obtain hot scientific research literature data, this invention first uses the ChatGpt model analysis tool to perform intelligent analysis of the persistence of popularity of the target scientific research literature data according to the set analysis tasks, and obtains the results of the persistence of popularity analysis; then, based on the hot trend prediction analysis tool, it performs intelligent analysis of the stability of popularity of the target scientific research literature data, and obtains the results of the stability of popularity analysis; finally, based on the results of the persistence of popularity analysis and the stability of popularity analysis, hot scientific research literature data is screened and obtained.

[0077] The beneficial effects of the above technical solution are as follows: By using the solution provided in this embodiment, the ChatGpt model analysis tool is combined with the hot topic trend prediction analysis tool to analyze the heat persistence and heat stability of the target scientific research literature data. Based on the analysis results, hot scientific research literature data can be screened and obtained.

[0078] In one embodiment, based on the ChatGpt model analysis tool, according to the set analysis tasks, intelligent analysis of the popularity persistence of target scientific literature data is performed to obtain the popularity persistence analysis results, including:

[0079] Obtain the ChatGpt model and set the analysis task for the ChatGpt model; the analysis task is to construct a question task involving the popularity of the target scientific literature data based on several set question templates.

[0080] Based on the ChatGpt model, intelligent analysis of the persistence of popularity of target scientific literature data is performed according to the analysis task, and several analysis results are obtained. The analysis results include the popularity persistence period, the probability of popularity decline, the probability of popularity rise, the probability of popularity being impacted, and the probability of popularity being cyclical.

[0081] The working principle of the above technical solution is as follows: In order to achieve intelligent analysis of the persistence of popularity of target scientific literature data and obtain the results of popularity persistence analysis, this invention first obtains the ChatGpt model and sets the analysis task for the ChatGpt model; the analysis task is a question task involving the popularity of target scientific literature data constructed according to several set question templates; then, based on the ChatGpt model, according to the analysis task, intelligent analysis of the persistence of popularity of target scientific literature data is performed to obtain several analysis results; the analysis results include the popularity persistence period, the probability of popularity decline, the probability of popularity rise, the probability of popularity being impacted, and the probability of popularity being cyclical.

[0082] The beneficial effects of the above technical solution are as follows: by using the solution provided in this embodiment, accurate results of heat persistence analysis can be obtained by intelligently analyzing the heat persistence of target scientific literature data, which provides conditions for the subsequent acquisition of scientific research hotspots.

[0083] In one embodiment, based on a hotspot trend prediction and analysis tool, intelligent analysis of the heat stability of target scientific literature data is performed to obtain heat stability analysis results, including:

[0084] A hotspot trend prediction and analysis tool based on convolutional neural networks;

[0085] Based on the hot topic trend prediction and analysis tool, the trend change of the popularity of target scientific research literature data is predicted, and the prediction results are obtained, including the popularity trend change curve.

[0086] The heat trend curve is fitted with the set heat stability trend curve to obtain the curve fitting degree, and the curve fitting degree is used as the result of the heat stability analysis.

[0087] The working principle of the above technical solution is as follows: In order to achieve intelligent analysis of the popularity stability of target scientific literature data and obtain the popularity stability analysis results, this invention first constructs a hot spot trend prediction and analysis tool based on a convolutional neural network; then, based on the hot spot trend prediction and analysis tool, it predicts the trend change of the popularity of the target scientific literature data and obtains the prediction results, which include the popularity trend change curve; then, it performs a fitting analysis between the popularity trend change curve and the set popularity stability trend change curve to obtain the curve fitting degree, and uses the curve fitting degree as the popularity stability analysis result.

[0088] The beneficial effects of the above technical solution are as follows: by using the solution provided in this embodiment, accurate heat stability analysis results can be obtained by intelligently analyzing the heat stability of the target scientific literature data.

[0089] In one embodiment, based on the results of popularity persistence analysis and popularity stability analysis, hot research literature data is screened and obtained, including:

[0090] Based on the results of the heat persistence analysis, the first set of target scientific literature data that meets the first set conditions is selected and obtained. The first set conditions are: the heat persistence period is greater than the set persistence period threshold, the probability of heat decrease is less than the set first probability threshold, the probability of heat increase is less than the set second probability threshold, the probability of heat being impacted is less than the set third probability threshold, and the probability of heat being cyclical is greater than the set fourth probability threshold.

[0091] Based on the results of the heat persistence analysis, a second set of target scientific literature data that meets the second set of conditions is selected; the second set of conditions is: the curve fit degree is greater than the set fit degree threshold.

[0092] Data that simultaneously belongs to both the first and second group of target research literature data is considered as hot research literature data.

[0093] The working principle of the above technical solution is as follows: In order to screen and obtain hot research literature data based on the results of heat persistence analysis and heat stability analysis, this invention first screens and obtains a first set of target research literature data that meets the first set conditions based on the heat persistence analysis results. The first set conditions are: the heat persistence period is greater than a set persistence period threshold, the probability of heat decrease is less than a set first probability threshold, the probability of heat increase is less than a set second probability threshold, the probability of heat being impacted is less than a set third probability threshold, and the probability of heat having a cyclical nature is greater than a set fourth probability threshold. Then, based on the heat persistence analysis results, a second set of target research literature data that meets the second set conditions is screened and obtained. The second set conditions are: the curve fitting degree is greater than a set fitting degree threshold. Finally, the data that simultaneously belongs to the first set of target research literature data and the second set of target research literature data are regarded as hot research literature data.

[0094] The beneficial effects of the above technical solution are as follows: by adopting the solution provided in this embodiment, hot scientific literature data can be obtained by screening based on the results of heat persistence analysis and heat stability analysis, which can ensure the accuracy of hot scientific literature data screening.

[0095] In one embodiment, a visualization analysis of popular scientific literature data is performed to obtain the results of the popularity visualization analysis, including:

[0096] Obtain visualization and analysis tools;

[0097] Visualization analysis tools are used to perform visualization analysis on hot research literature data to obtain hotspot visualization analysis results.

[0098] The working principle of the above technical solution is as follows: In order to realize the visualization analysis of hot scientific research literature data, the present invention first obtains a visualization analysis tool; then, it uses the visualization analysis tool to perform visualization analysis on the hot scientific research literature data and obtains the hot visualization analysis results.

[0099] The beneficial effects of the above technical solution are as follows: by using the solution provided in this embodiment, and by utilizing visualization analysis tools to perform visualization analysis on hot scientific literature data, accurate hotspot visualization analysis results can be obtained.

[0100] Institutional research hotspot tracking and visualization analysis system, such as Figure 3 As shown, it includes:

[0101] The data acquisition module is used to acquire scientific literature data from multiple dimensions;

[0102] The target scientific literature data acquisition module is used to perform cluster analysis and popularity mining on scientific literature data to obtain target scientific literature data with potential hot spots.

[0103] Hot research literature data is used to track and analyze the continuity and stability of the popularity of target research literature data, and to filter and obtain hot research literature data.

[0104] The Hotspot Visualization Analysis module is used to perform visual analysis on hot research literature data and obtain hotspot visualization analysis results.

[0105] The working principle of the above technical solution is as follows: In order to realize the institutional scientific research hotspot tracking and visualization analysis system, this invention proposes a data acquisition module for acquiring scientific research literature data from multiple dimensions; a target scientific research literature data acquisition module for performing cluster analysis and popularity mining on scientific research literature data to obtain target scientific research literature data with potential hotspots; a hotspot scientific research literature data module for tracking and analyzing the popularity persistence and stability of target scientific research literature data, and filtering to obtain hotspot scientific research literature data; and a hotspot visualization analysis module for performing visualization analysis on hotspot scientific research literature data to obtain hotspot visualization analysis results.

[0106] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, scientific research literature data is acquired from multiple dimensions, and the heat continuity and heat stability of target scientific research literature data with potential hot spots are tracked and analyzed to obtain hot scientific research literature data. Finally, visualization analysis is performed, which can improve the accuracy of scientific research hot spot acquisition and visualization effect.

[0107] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An institution research hotspot tracking and visualization analysis method, characterized in that, Comprise: Multi-dimensional acquisition of scientific literature data; Specifically, based on the constructed data acquisition tool, scientific literature data is acquired from multiple dimensions; Multiple dimensions include scientific research institutions, research teams, research fields, and regions; Cluster analysis and heat mining of scientific literature data to obtain target scientific literature data with potential hotspots; Specifically: Cluster analysis of scientific literature data to obtain research direction category group data and theme category group data to which the research direction category group belongs, forming a plurality of group data pairs; Using a data mining tool, the data attention of the group data pair is mined to obtain a first group data pair; The data mining tool includes tools related to data statistics, data retrieval, machine learning, expert systems, and pattern recognition; Filtering the data credibility of the first group data pair to obtain a second group data pair with a credibility greater than a set credibility threshold, and the second group data pair is used as target scientific literature data with potential hotspots; Tracking analysis of the heat duration and heat stability of the target scientific literature data, and filtering to obtain hot scientific literature data; Specifically: Obtain the ChatGpt model and set the analysis task for the ChatGpt model; The analysis task is to construct a question task related to the heat of the target scientific literature data according to a plurality of question templates set; Based on the ChatGpt model, the heat duration of the target scientific literature data is intelligently analyzed according to the analysis task to obtain a plurality of analysis results; The analysis results include heat duration period, heat decline probability, heat rise probability, heat impact probability, and heat recyclable probability; Based on the convolutional neural network, a hot trend prediction analysis tool is constructed; Based on the hot trend prediction analysis tool, the heat trend of the target scientific literature data is predicted to obtain a prediction result, which includes a heat trend curve; Fitting analysis of the heat trend curve and the set heat stable trend curve to obtain a curve fitting degree, which is used as a heat stability analysis result; According to the heat duration analysis result, a first group of target scientific literature data meeting the first set condition is filtered; The first set condition is that the heat duration period is greater than a set duration period threshold, and the heat decline probability is less than a set first probability threshold, and the heat rise probability is less than a set second probability threshold, and the heat impact probability is less than a set third probability threshold, and the heat recyclable probability is greater than a set fourth probability threshold; According to the heat stability analysis result, a second group of target scientific literature data meeting the second set condition is filtered; The second set condition is that the curve fitting degree is greater than a set fitting degree threshold; Data belonging to both the first group of target scientific literature data and the second group of target scientific literature data are used as hot scientific literature data; Visual analysis of the hot scientific literature data to obtain a hot visual analysis result.

2. The method of claim 1, wherein, Visual analysis of the hot scientific literature data to obtain a hot visual analysis result, including: Obtain a visual analysis tool; The hot spot research literature data is visualized by using a visual analysis tool to obtain a hot spot visual analysis result.

3. The system for tracking and visualizing research hotspots of an organization, characterized in that, The method comprises the following steps: a data acquisition module is configured to acquire research literature data in multiple dimensions, specifically, the research literature data is acquired in multiple dimensions based on a constructed data acquisition tool, the multiple dimensions including research institutions, research teams, research fields, and regions; a target research literature data acquisition module is configured to perform clustering analysis and heat mining on the research literature data to obtain target research literature data with potential hot spots, specifically: the research literature data is clustered to obtain research direction category group data and theme category group data to which the research direction category group data belongs, forming a plurality of group data pairs; a data mining tool is used to mine the data attention degree of the group data pairs to obtain a first group data pair, the data mining tool including tools related to data statistics, data retrieval, machine learning, expert systems, and pattern recognition; the first group data pair is screened for data credibility, and a second group data pair with a credibility greater than a set credibility threshold is obtained, and the second group data pair is taken as target research literature data with potential hot spots; hot spot research literature data is used to track and analyze the heat persistence and heat stability of the target research literature data, and hot spot research literature data is obtained by screening, specifically: a ChatGpt model is obtained, and an analysis task for the ChatGpt model is set; the analysis task is a question task related to the heat of the target research literature data constructed according to a plurality of question templates; based on the ChatGpt model, the heat persistence of the target research literature data is intelligently analyzed according to the analysis task, and a plurality of analysis results are obtained; the analysis results include heat persistence period, heat decline probability, heat rise probability, heat impact probability, and heat recyclable probability; a hot spot trend prediction analysis tool is constructed based on a convolutional neural network; based on the hot spot trend prediction analysis tool, the heat trend of the target research literature data is predicted, and a prediction result is obtained, the prediction result including a heat trend change curve; the heat trend change curve is fitted with a set heat stable trend change curve to obtain a curve fitting degree, and the curve fitting degree is taken as a heat stability analysis result; the first group target research literature data meeting the first set condition is obtained by screening according to the heat persistence analysis result; the first set condition is that the heat persistence period is greater than a set persistence period threshold, the heat decline probability is less than a set first probability threshold, the heat rise probability is less than a set second probability threshold, the heat impact probability is less than a set third probability threshold, and the heat recyclable probability is greater than a set fourth probability threshold; the second group target research literature data meeting the second set condition is obtained by screening according to the heat stability analysis result; the second set condition is that the curve fitting degree is greater than a set fitting degree threshold; data belonging to both the first group target research literature data and the second group target research literature data is taken as hot spot research literature data. The hotspot visualization analysis module is configured to perform visualization analysis on the hotspot scientific literature data to obtain a hotspot visualization analysis result. The hotspot visualization analysis module is configured to perform visualization analysis on the hotspot scientific literature data to obtain a hotspot visualization analysis result.

Images