Green consumption behavior analysis method and device, equipment and storage medium

Abstract

The invention discloses a green consumption behavior analysis method and device, equipment and a storage medium, and relates to the technical field of data analysis, and the method comprises the steps: obtaining user consumption behavior data of a plurality of green consumption products from a green consumption behavior database; determining platform economic data of the target platform according to the platform investigation information and the platform economic measurement index of the target platform; constructing a target bidirectional feedback conduction mechanism based on the user consumption behavior data and the platform economic data through a simultaneous equation model; and performing simulation analysis on the target bidirectional feedback conduction mechanism to obtain a green consumption behavior analysis result. By applying the scheme, the technical problems that in the prior art, platform economy usually only pays attention to a business model and ignores consumer behaviors, and the social dynamics and complexity of green consumer behaviors cannot be researched by fully considering the limited rationality and adaptability of consumer subject behaviors are solved.

Description

Technical Field

[0001] The present application relates to the field of data analysis technology, and in particular to a method, device, equipment and storage medium for analyzing green consumer behavior. Background Art

[0002] As global environmental problems become increasingly serious, green consumption, as a consumer behavior that implements the concept of green and low-carbon throughout the entire process, aims to choose environmentally friendly and low-carbon products and services, reduce the impact of consumer behavior on the environment, and achieve harmonious coexistence between man and nature. At the same time, the rise of platform economy has a natural advantage in promoting green consumption by stimulating consumers' environmental sentiments, improving consumers' green consumption experience, shaping consumers' green consumption behavior, and forming a positive feedback mechanism through information, evaluation and incentive systems. However, at present, platform economy usually only focuses on business models and ignores consumer behavior, and fails to fully consider the limited rationality and adaptability of consumer behavior to study the social dynamics and complexity of green consumption behavior.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide a green consumer behavior analysis method, device, equipment and storage medium, aiming to solve the technical problems in the prior art that platform economy usually only focuses on business models and ignores consumer behavior, and fails to fully consider the limited rationality and adaptability of consumer behavior to study the social dynamics and complexity of green consumer behavior.

[0005] To achieve the above objectives, this application proposes a green consumption behavior analysis method, which includes:

[0006] Obtaining user consumption behavior data of a number of green consumption products from a green consumption behavior database;

[0007] Determine the platform economic data of the target platform based on the platform research information and platform economic measurement indicators of the target platform;

[0008] A target two-way feedback transmission mechanism is constructed based on the user consumption behavior data and the platform economy data through a simultaneous equation model, wherein the target two-way feedback transmission mechanism is a two-way feedback transmission mechanism between the platform economy and the user's green consumption behavior under the guidance of the platform economy;

[0009] The target two-way feedback transmission mechanism is simulated and analyzed to obtain the analysis results of green consumption behavior.

[0010] In one embodiment, before the step of acquiring user consumption behavior data of a plurality of green consumption products from the green consumption behavior database, the step further includes:

[0011] Determine the data collection scope and data collection target, wherein the data collection target includes: green product opinion data and sentiment tendency data based on user feedback on a number of green consumer products;

[0012] Collecting the green product opinion data and the sentiment tendency data within the data collection range through a preset data collector;

[0013] A green consumption behavior database is constructed based on the green product opinion data and the sentiment tendency data.

[0014] In one embodiment, the step of constructing a target two-way feedback transmission mechanism based on the user consumption behavior data and the platform economic data through a simultaneous equation model includes:

[0015] Classify the user consumption behavior data through a preset text classification model to obtain green product attribute data, green emotion attribute data and green service attribute data corresponding to each green consumer product;

[0016] Acquire infrastructure assessment data, information assessment data and platform transaction data of the target platform based on the platform economic data;

[0017] A target two-way feedback transmission mechanism is constructed through a simultaneous equation model based on the green product attribute data, the green emotional attribute data, the green service attribute data, the infrastructure evaluation data, the informationization evaluation data and the platform transaction data.

[0018] In one embodiment, the step of constructing a target two-way feedback transmission mechanism based on the green product attribute data, the green emotion attribute data, the green service attribute data, the infrastructure evaluation data, the information evaluation data and the platform transaction data through a simultaneous equation model includes:

[0019] Determine, by a simultaneous equation model, first impact feedback corresponding to the green product attribute data, the green sentiment attribute data, and the green service attribute data on the infrastructure evaluation data, the informationization evaluation data, and the platform transaction data;

[0020] Determine, by means of the simultaneous equation model, second impact feedback corresponding to the infrastructure assessment data, the information assessment data and the platform transaction data on the green product attribute data, the green emotion attribute data and the green service attribute data;

[0021] A target bidirectional feedback transmission mechanism is constructed based on the first impact feedback and the second impact feedback.

[0022] In one embodiment, before the step of acquiring user consumption behavior data of a plurality of green consumption products from the green consumption behavior database, the step further includes:

[0023] Collecting green consumption behavior sample data from a web page by using a web crawler, wherein the green consumption behavior sample data includes product data, sentiment data, and service data of the web page;

[0024] Performing sample identification configuration on the green consumption behavior sample data, wherein the sample identification is used to characterize the data category of the green consumption behavior sample data;

[0025] Performing word segmentation processing on the green consumption behavior sample data to obtain a first word segmentation result corresponding to the product data, a second word segmentation result corresponding to the sentiment data, and a third word segmentation result corresponding to the service data;

[0026] Obtaining a feature vector corresponding to the green consumption behavior sample data based on the first word segmentation result, the second word segmentation result, and the third word segmentation result;

[0027] The initial text classification model is trained using the feature vector to obtain a preset text classification model.

[0028] In one embodiment, the step of obtaining a feature vector corresponding to the green consumption behavior sample data based on the first word segmentation result, the second word segmentation result, and the third word segmentation result includes:

[0029] Perform keyword extraction on the first word segmentation result, the second word segmentation result, and the third word segmentation result respectively through the TF-IDF matrix to obtain product data keywords, sentiment data keywords, and service data keywords;

[0030] Obtaining a first keyword vector corresponding to the product data keyword, a second keyword vector corresponding to the emotion data keyword, and a third keyword vector corresponding to the service data keyword through a preset word vector model;

[0031] A feature vector corresponding to the green consumption behavior sample data is obtained based on the first keyword vector, the second keyword vector, and the third keyword vector.

[0032] In one embodiment, the step of simulating and analyzing the target two-way feedback transmission mechanism to obtain the green consumption behavior analysis result includes:

[0033] Performing simulation analysis on the target bidirectional feedback transmission mechanism to obtain model simulation results;

[0034] Determine the user's green consumption tendency, green consumption decision and green consumption behavior based on the model simulation results;

[0035] A green consumption behavior analysis result is generated based on the green consumption tendency, the green consumption decision and the green consumption behavior.

[0036] In addition, to achieve the above purpose, the present application also proposes a green consumption behavior analysis device, the device comprising:

[0037] A behavior data acquisition module is used to acquire user consumption behavior data of a number of green consumption products from a green consumption behavior database;

[0038] An economic data determination module, used to determine the platform economic data of the target platform based on the platform research information and platform economic measurement indicators of the target platform;

[0039] A transmission mechanism construction module, used to construct a target two-way feedback transmission mechanism based on the user consumption behavior data and the platform economy data through a simultaneous equation model, wherein the target two-way feedback transmission mechanism is a two-way feedback transmission mechanism between the platform economy and the user's green consumption behavior under the guidance of the platform economy;

[0040] The consumption behavior analysis module is used to simulate and analyze the target two-way feedback transmission mechanism to obtain green consumption behavior analysis results.

[0041] In addition, to achieve the above-mentioned purpose, the present application also proposes a green consumption behavior analysis device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the green consumption behavior analysis method described above.

[0042] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the green consumption behavior analysis method described above are implemented.

[0043] The present application provides a method for analyzing green consumption behavior, which discloses obtaining user consumption behavior data of several green consumption products from a green consumption behavior database; determining the platform economic data of the target platform according to the platform survey information and platform economic measurement indicators of the target platform; constructing a target two-way feedback transmission mechanism based on the user consumption behavior data and the platform economic data through a simultaneous equation model, wherein the target two-way feedback transmission mechanism is a two-way feedback transmission mechanism between the platform economy and the user's green consumption behavior under the dominance of the platform economy; simulating and analyzing the target two-way feedback transmission mechanism to obtain a green consumption behavior analysis result; since the present invention can construct a target two-way feedback transmission mechanism based on the user's consumption behavior data and the platform economic data through a simultaneous equation model, and obtain a green consumption behavior analysis result according to the simulation analysis result of the target two-way feedback transmission mechanism, the technical problem that the platform economy in the prior art usually only focuses on the business model and ignores the consumer behavior, and fails to fully consider the limited rationality and adaptability of the consumer subject's behavior to study the social dynamics and complexity of the green consumption behavior is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0046] Figure 1 A flow chart of the first embodiment of the green consumption behavior analysis method of the present application;

[0047] Figure 2 This is an example diagram of the behavior data of green consumer products in the green consumer behavior analysis method of this application;

[0048] Figure 3 This is an example diagram of sentiment tendency data in the green consumption behavior analysis method of this application;

[0049] Figure 4 A flow chart of the second embodiment of the green consumption behavior analysis method of the present application;

[0050] Figure 5 This is a schematic diagram of the overall process of the green consumption behavior analysis method for this application;

[0051] Figure 6 A flow chart of the third embodiment of the green consumption behavior analysis method of the present application;

[0052] Figure 7 This is a schematic diagram of the module structure of the green consumption behavior analysis device according to an embodiment of the present application;

[0053] Figure 8 This is a schematic diagram of the device structure of the hardware operating environment involved in the green consumption behavior analysis method in the embodiment of the present application.

[0054] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0055] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0056] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0057] The main solution of the embodiment of the present application is: obtaining user consumption behavior data of several green consumption products from a green consumption behavior database; determining the platform economic data of the target platform according to the platform survey information and platform economic measurement indicators of the target platform; constructing a target two-way feedback transmission mechanism based on the user consumption behavior data and the platform economic data through a simultaneous equation model, and the target two-way feedback transmission mechanism is a two-way feedback transmission mechanism between the platform economy and the user's green consumption behavior under the dominance of the platform economy; simulating and analyzing the target two-way feedback transmission mechanism to obtain green consumption behavior analysis results.

[0058] The present application provides a solution, which makes it possible to construct a target two-way feedback transmission mechanism based on the user's consumption behavior data and platform economic data through a simultaneous equation model, and obtain green consumption behavior analysis results based on the simulation analysis results of the target two-way feedback transmission mechanism, thereby solving the technical problems in the prior art that platform economy usually only focuses on business models and ignores consumer behavior, and fails to fully consider the limited rationality and adaptability of consumer subject behavior to study the social dynamics and complexity of green consumption behavior.

[0059] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a green consumer behavior analysis device, etc. The following takes the green consumer behavior analysis device as an example (hereinafter referred to as the device) to illustrate this embodiment and the following embodiments.

[0060] Based on this, the present application embodiment provides a green consumption behavior analysis method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the green consumption behavior analysis method of the present application.

[0061] In this embodiment, the green consumption behavior analysis method includes steps S10 to S40:

[0062] Step S10: Acquire user consumption behavior data of a number of green consumption products from a green consumption behavior database.

[0063] It should be noted that as the integration of the digital economy and the real economy continues to deepen, the rise of online platforms has changed the pattern of green consumption among consumers. More consumers choose to purchase products and services online. Furthermore, this plan starts with the platform economy and the two-way feedback mechanism to obtain first-hand green consumption data, which makes the research results more real and objective for studying green consumption behavior. From the perspective of platform guidance, online green consumption and platforms are more reflected in a symbiotic relationship of mutual selection and mutual promotion. On the one hand, the "green" awareness of the platform is formed and disseminated through the innovation of product and service models, and the concept of green consumption is transmitted to the public in the interaction with consumers, thus guiding platform consumers to enhance their awareness of green consumption. On the other hand, based on the proposal of my country's carbon peak and carbon neutrality goals, with the further improvement of the overall green, low-carbon and environmental protection concepts of the society, the demand for green consumption from the perspective of consumers is increased, which in turn promotes the green transformation of platform products and services, and reversely affects the green practice of the platform. From the output of green concepts from the platform to the active selection trend of consumers for platform green products and services, products and services with platform green attributes will also be actively selected by consumers, which has a two-way promotion effect. Therefore, the development of green consumption requires the joint promotion and support of all market players, while at the same time providing guidance from macro policies to further clarify the direction of development.

[0064] It is understandable that the green consumption behavior database may be a database for storing green consumption behavior data of users. Green consumption behavior may be the behavior taken by consumers in the process of purchasing and using goods and services to reduce negative impacts on the environment, protect ecosystems and promote sustainable development. Accordingly, green consumption behavior data may be data generated by users when they perform green consumption behaviors.

[0065] It should be noted that the green consumer products mentioned above can be products that are purchased or used by users and conform to the green consumer concept. Figure 2 , Figure 2 This is an example diagram of the behavior data of green consumer products in the green consumer behavior analysis method of this application. Figure 2As shown, the green consumer products in this solution may include but are not limited to low-carbon transportation, environmentally friendly furniture, energy-saving lamps, node computers, water-saving products, degradable biological products, green packaging, green textiles, green building materials, solar photovoltaic equipment, new energy vehicles, organic food and recycled paper, etc. In actual applications, the device can use a crawler method to collect data generated by users when purchasing or using these green consumer products (such as comment data for the green consumer products) on the web page, obtain the consumer behavior data corresponding to the green consumer products, and store these consumer behavior data in the corresponding files.

[0066] Furthermore, before step S10, it also includes: determining a data collection scope and a data collection target, wherein the data collection target includes: green product opinion data and sentiment tendency data based on user feedback on a number of green consumer products; collecting the green product opinion data and the sentiment tendency data within the data collection scope by a preset data collector; and constructing a green consumer behavior database based on the green product opinion data and the sentiment tendency data.

[0067] It should be understood that the above data collection scope may be the scope of collecting user behavior data. In this embodiment, the data collection scope may be the green product data in the social platform or the green product shopping platform data scope, and this embodiment does not limit this.

[0068] It should be noted that the above data collection target can be the user's opinions and emotional tendency data on green consumer products, that is, the above green product opinion data and emotional tendency data, where the green product opinion data can be the opinions or comments data expressed by the user on green consumer products; the emotional tendency data can be the data used to characterize the user's emotional attitude towards green consumer products, such as: good, bad, energy-saving, not energy-saving, etc., which is not limited in this embodiment. In this embodiment, refer to Figure 3 , Figure 3 This is an example diagram of sentiment tendency data in the green consumption behavior analysis method of this application. Figure 3 As shown, the device can collect comment data posted by users on a social platform, such as: "Green travel is really important, I like it", "I won't buy it, it's expensive and not worth it", "Complete the task efficiently and save energy", and extract keywords from these data, extract and process keywords such as "green travel", "like", "expensive", "not worth it", and "energy saving", and classify these keywords, such as "like", "expensive", "not worth it", and "energy saving" can be classified as sentiment tendency data.

[0069] In practical applications, after obtaining users' comments on green consumer products, the comment data can be classified, and after obtaining green product opinion data and emotional tendency data, these data can be classified and stored in the green consumer behavior database.

[0070] Step S20: Determine the platform economic data of the target platform based on the platform research information and platform economic measurement indicators of the target platform.

[0071] It should be noted that the platform economy is a new economic system based on digital technology, consisting of economic activity units driven by data, supported by platforms, and coordinated by networks. It achieves efficient allocation of resources and value creation by connecting multilateral markets. At present, research on the platform economy mainly focuses on two dimensions: one is the analysis and discussion at the theoretical level, including transaction cost theory, bilateral market theory, network effect theory, and the popularization of functions such as precision marketing and personalized recommendations driven by data; the other is the analysis of economic effects at the empirical level, mainly focusing on efficiency improvement and economies of scale, platform innovation, and technological progress. By providing innovative business models and services, the platform economy can expand the user scale, achieve cost sharing and rapid growth of revenue, and promote technological progress and industrial upgrading.

[0072] It should be understood that the above-mentioned target platform may be any purchasing platform or use platform of green consumer products, and this embodiment does not impose any limitation on this.

[0073] It should be noted that the above platform economy measurement indicators can be indicators used to define and measure platform economy. The platform economy measurement indicators in this embodiment can include but are not limited to infrastructure, informatization and transaction volume. Accordingly, the above platform economy data can be data used to evaluate the development level of platform economy, such as infrastructure evaluation data, the informatization evaluation data and the platform transaction data.

[0074] Step S30: constructing a target two-way feedback transmission mechanism based on the user consumption behavior data and the platform economy data through a simultaneous equation model, wherein the target two-way feedback transmission mechanism is a two-way feedback transmission mechanism between the platform economy and the user's green consumption behavior under the guidance of the platform economy.

[0075] It should be noted that the simultaneous equation model can be used to describe the mutual influence relationship between multiple variables in the economic system. In the simultaneous equation model, the various variables in the economic system are not only unilaterally determined or determined, but are interrelated and mutually influenced. These variables may appear as explained variables and explanatory variables at the same time, which reflects the mutual dependence between variables in the economic system. This mutual dependence requires the use of more complex estimation methods to estimate model parameters, such as iteration method, system estimation method, etc. In addition, the simultaneous equation model can be further classified according to the different relationships between variables in the economic system, such as linear simultaneous equation model and nonlinear simultaneous equation model. In practical applications, researchers need to choose the appropriate model form and estimation method according to specific research problems and data characteristics.

[0076] It should be noted that the target two-way feedback transmission mechanism can be a mechanism in which green consumption behavior is not only affected by the platform mechanism, but also consumers' purchasing behavior will reversely affect the platform strategy. In practical applications, in a rapidly changing market environment, consumers' preferences and needs are also constantly evolving. The platform constantly adjusts and optimizes green policies based on consumer feedback, thus forming a virtuous circle. On the one hand, the green consumption incentive policies launched by the platform (such as points redemption, cash back, etc.) encourage consumers to make more green consumption; on the other hand, consumers' purchasing behavior will be fed back to the platform, and the platform will make dynamic adjustments based on real-time feedback, prompting it to further optimize incentives.

[0077] In practical applications, the literature on platform economy and green consumption issues is more of a normative theoretical discussion at this stage, and there are very few micro-sample studies on green consumption behavior. Normative theoretical analysis can sort out its driving factors and internal mechanisms, but it fails to fully consider the limited rationality and adaptive behavior of the behavioral consumer subject, and needs to be tested through empirical data to obtain more accurate conclusions. Therefore, this scheme can carry out text classification technology based on machine learning to study the social dynamics and complexity of green consumption behavior and provide a basis for the formulation of green consumption policies. In addition, since the current research focuses mostly on the impact of the demand side (consumers), and rarely explores the coordinated changes of the platform side-demand side (consumers), this scheme can deeply explore the two-way feedback relationship between the platform economy and residents' green consumption behavior, explore the incentive policy of the two-way feedback mechanism of green consumption behavior under the dominance of the platform economy, and then effectively propose countermeasures for the sustainable development of green consumption behavior and platform economy.

[0078] In this embodiment, the device can combine relevant theories such as multiple determination theory, planned behavior theory, two-sided market theory, transaction cost theory, resource allocation theory, and two-way feedback mechanism, and use a simultaneous equation model based on user consumption behavior data and platform economic data to describe the mutual influence relationship between green consumption behavior and platform economy (i.e., two-way feedback relationship), and construct a target two-way feedback transmission mechanism based on the two-way feedback relationship between green consumption behavior and platform economy.

[0079] Step S40: Simulate and analyze the target two-way feedback transmission mechanism to obtain green consumption behavior analysis results.

[0080] Furthermore, the step S40 includes: performing simulation analysis on the target two-way feedback transmission mechanism to obtain model simulation results; determining the user's green consumption tendency, green consumption decision and green consumption behavior based on the model simulation results; and generating green consumption behavior analysis results based on the green consumption tendency, the green consumption decision and the green consumption behavior.

[0081] It should be noted that the above-mentioned green consumption behavior analysis results can be obtained by simulating the two-way feedback mechanism of residents' green consumption behavior under the dominance of platform economy, and the analysis results of the impact of platform economy on users' green consumption tendencies, consumption decisions and consumption behaviors, as well as the impact of green consumption on the three dimensions of platform economy (infrastructure, information, and transaction volume).

[0082] In this embodiment, the device can propose countermeasures for the sustainable development of green consumer behavior and platform economy according to the analysis results of green consumer behavior, such as: strengthening market promotion, optimizing supply chain, actively innovating, and policy support. Among them, strengthening market promotion is because green consumer products have certain market power and strong future development power, and consumers have certain environmental awareness, and the willingness to pay for green consumer products is positively correlated with environmental awareness. Therefore, through education and publicity activities, consumers' awareness of green consumer products can be improved, and more consumers can be attracted to pay attention to green consumer products, so as to convert potential consumer groups into consumer groups and drive the consumption of green consumer products. Optimizing the supply chain and actively innovating is manifested in that enterprises optimize the supply chain by adopting green supply chain management practices to improve resource utilization efficiency, reduce product production costs, and then reduce prices, so as to increase sales. Encourage enterprises to invest in research and development, improve product performance, improve its safety and durability, and increase product diversity, so as to develop more green consumer products that meet market demand, meet the needs of different consumer groups, and also increase product sales. At the same time, enterprises must also resolutely implement the basic policy of practicality and the basic principle of respecting originality. Policy support is manifested in suggesting that the government provide incentives such as tax incentives and financial subsidies to encourage enterprises to produce green consumer products. At the same time, more policies on green environmental protection will be introduced, including green economy, green tourism, green culture, etc., to guide enterprises to make innovative decisions on green consumer products and promote the development of green consumer products; encourage the public to pay attention to green topics and promote the sales of green consumer products.

[0083] The present embodiment provides a green consumption behavior analysis method, which discloses obtaining user consumption behavior data of several green consumption products from a green consumption behavior database; determining the platform economy data of the target platform according to the platform survey information and platform economy measurement indicators of the target platform; constructing a target two-way feedback transmission mechanism based on the user consumption behavior data and the platform economy data through a simultaneous equation model, and the target two-way feedback transmission mechanism is a two-way feedback transmission mechanism between the platform economy and the user's green consumption behavior under the dominance of the platform economy; simulating and analyzing the target two-way feedback transmission mechanism to obtain a green consumption behavior analysis result; because the present embodiment can construct a target two-way feedback transmission mechanism based on the user's consumption behavior data and the platform economy data through a simultaneous equation model, and obtain the green consumption behavior analysis result according to the simulation analysis result of the target two-way feedback transmission mechanism, thereby solving the technical problem that the platform economy in the prior art usually only focuses on the business model and ignores the consumer behavior, and fails to fully consider the limited rationality and adaptability of the consumer subject's behavior to study the social dynamics and complexity of the green consumption behavior.

[0084] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 4 , Figure 4 A flow chart of the second embodiment of the green consumption behavior analysis method of this application is provided.

[0085] In this embodiment, step S30 includes steps S301 to S303:

[0086] Step S301: classify the user consumption behavior data through a preset text classification model to obtain green product attribute data, green emotion attribute data and green service attribute data corresponding to each green consumption product.

[0087] It should be understood that the above-mentioned preset text classification model can be a model for classifying different types of consumer behavior data. In this embodiment, the types of user consumer behavior data can include but are not limited to green product attribute data, green emotional attribute data and green service attribute data, wherein the green product attribute data can be data used to highlight the green attributes of the product, such as: "green", "ecological", "environmental protection", "energy saving", "sustainable" and other data; the green emotional attribute data can be data used to characterize the user's emotions towards green products, such as: "recommendation", "like", "beautiful", "good reviews" and other data; the green service attribute data can be data used to provide services to users when purchasing or using green products, such as: "shipping", "customer service", "details page", "reply", "suggestion" and other data.

[0088] Step S302: Acquire infrastructure assessment data, information assessment data and platform transaction data of the target platform based on the platform economic data.

[0089] It is understandable that the above-mentioned infrastructure assessment data can be data used to assess the infrastructure level of the platform; the above-mentioned information assessment data can be data used to assess the information level of the platform; and the above-mentioned platform transaction data can be data used to assess the transaction volume of the platform. In this embodiment, the device can collect the operation data of the platform and the interaction data between the platform and the user in real time, including: the platform's browsing volume, the user's consumption data on the platform, etc., and then integrate these data to obtain platform economic data. When conducting green consumption behavior analysis later, the platform economic data can be extracted and classified to obtain the infrastructure assessment data, information assessment data and platform transaction data of the target platform.

[0090] Step S303: constructing a target two-way feedback transmission mechanism based on the green product attribute data, the green emotion attribute data, the green service attribute data, the infrastructure evaluation data, the informationization evaluation data and the platform transaction data through a simultaneous equation model.

[0091] Specifically, the step S303 includes: determining the first impact feedback corresponding to the green product attribute data, the green emotion attribute data and the green service attribute data on the infrastructure evaluation data, the informationization evaluation data and the platform transaction data through the simultaneous equation model; determining the second impact feedback corresponding to the infrastructure evaluation data, the informationization evaluation data and the platform transaction data on the green product attribute data, the green emotion attribute data and the green service attribute data through the simultaneous equation model; and constructing a target two-way feedback transmission mechanism based on the first impact feedback and the second impact feedback.

[0092] It should be noted that the first impact feedback can be the impact feedback that characterizes the user's green consumption behavior on the platform economy. In this embodiment, the device can input green product attribute data, green emotional attribute data, green service attribute data, infrastructure assessment data, information assessment data and platform transaction data into the simultaneous equation model to describe the association and impact relationship between these data through the simultaneous equation model, so as to determine the impact of the green product attribute data, green emotional attribute data and green service attribute data generated by the user when performing green consumption on the infrastructure assessment data, information assessment data and platform transaction data in the platform economy, and then determine the impact of the user's green consumption behavior on the platform economy.

[0093] It should be noted that the second impact feedback can be the impact feedback that characterizes the platform economy on the user's green consumption behavior. In this embodiment, the device can input green product attribute data, green emotional attribute data, green service attribute data, infrastructure evaluation data, information evaluation data and platform transaction data into the simultaneous equation model to describe the association and influence relationship between these data through the simultaneous equation model, so as to determine the impact of infrastructure, informationization and platform transaction volume in the platform economy on the user's consumption tendency, consumption decision and consumption behavior when performing green consumption, and further determine the impact of the platform economy on the user's green consumption behavior.

[0094] In the specific implementation, refer to Figure 5 , Figure 5 This is a schematic diagram of the overall process of the green consumption behavior analysis method of this application. Figure 5As shown, first, this scheme can be based on relevant theories such as multiple determination theory, planned behavior theory, two-sided market theory, transaction cost theory, resource allocation theory, etc., combined with a two-way feedback mechanism, to analyze the connotation and influencing factors of green consumption behavior, as well as the connotation and economic effect of platform economy. Then, the device can use machine learning text classification technology to perform consumer text sentiment analysis on user green consumption behavior data to obtain user product opinion data and sentiment tendency data for green consumer products, and build a green consumption behavior database. At the same time, the device can quantitatively measure the platform survey information to determine the measurement indicators that affect the platform economy. When analyzing green consumption behavior, the device can input green product attribute data, green sentiment attribute data and green service attribute data corresponding to green consumer products, as well as the platform's infrastructure evaluation data, information evaluation data and platform transaction data into the simultaneous equation model, so as to explore the first impact feedback of users' green consumption behavior on the platform economy, and the second impact feedback of the platform economy on users' green consumption behavior through the simultaneous equation model, and build a target two-way feedback transmission mechanism between the platform economy and the user's green consumption behavior under the leadership of the platform economy based on the first impact feedback and the second impact feedback. Then, the device can build a green consumption simulation model, the decision rules of which are the user's consumption tendency, consumption decision and consumption behavior, and simulate and analyze the target two-way feedback transmission mechanism based on the green consumption simulation model to obtain the analysis results of green consumption behavior. Finally, based on the analysis results of green consumption behavior, users can make decisions that are conducive to the sustainable development of green consumption behavior and platform economy, such as: for the government, green environmental protection education can be strengthened to encourage green service innovation; for the platform, green knowledge training can be strengthened to enrich the supply of green products.

[0095] In this embodiment, it is disclosed that user consumption behavior data is classified through a preset text classification model to obtain green product attribute data, green emotional attribute data and green service attribute data corresponding to each green consumer product; infrastructure assessment data, information assessment data and platform transaction data of the target platform are obtained according to platform economic data; a target two-way feedback transmission mechanism is constructed based on green product attribute data, green emotional attribute data, green service attribute data, infrastructure assessment data, information assessment data and platform transaction data through a simultaneous equation model. In this embodiment, a two-way feedback mechanism for users' green consumption behavior under the dominance of platform economy is obtained, which is conducive to promoting the sustainable development of green consumption and platform economy, while enhancing the competitiveness and market position of the platform.

[0096] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction, and will not be described in detail later. Figure 6 , Figure 6A flow chart of the third embodiment of the green consumption behavior analysis method of this application is provided.

[0097] In this embodiment, before step S10, the method further includes steps S01 to S05:

[0098] Step S01: collecting green consumption behavior sample data from a web page by using a web crawler, wherein the green consumption behavior sample data includes product data, emotion data, and service data of the web page.

[0099] It should be understood that the green consumption behavior sample data can be green consumption behavior data used to train the text classification model. In this embodiment, a web crawler can be used to collect a number of user posting data about green consumption products on a social platform or a web page, and the data includes product data, sentiment data, and service data. Then, the data can be cleaned and preprocessed to reduce the repetition rate of the data, thereby obtaining the final green consumption behavior sample data.

[0100] Step S02: configuring a sample identifier for the green consumption behavior sample data, wherein the sample identifier is used to characterize the data category of the green consumption behavior sample data.

[0101] It is understandable that the sample identifier can be an identifier for distinguishing data categories of green consumer behavior sample data. In this embodiment, corresponding sample identifiers can be set for green consumer behavior sample data based on different data categories in the green consumer behavior sample data by manual labeling to distinguish these data categories.

[0102] Step S03: performing word segmentation processing on the green consumption behavior sample data to obtain a first word segmentation result corresponding to the product data, a second word segmentation result corresponding to the sentiment data, and a third word segmentation result corresponding to the service data.

[0103] It should be understood that the above-mentioned first word segmentation result can be the result obtained after splitting the product data into several short sentences or words; accordingly, the above-mentioned second word segmentation result can be the result obtained after splitting the emotional data into several short sentences or words; the above-mentioned third word segmentation result can be the result obtained after splitting the service data into several short sentences or words.

[0104] Step S04: obtaining a feature vector corresponding to the green consumption behavior sample data based on the first word segmentation result, the second word segmentation result, and the third word segmentation result.

[0105] Specifically, the step S04 includes: performing keyword extraction on the first word segmentation result, the second word segmentation result and the third word segmentation result respectively through the TF-IDF matrix to obtain product data keywords, sentiment data keywords and service data keywords; obtaining the first keyword vector corresponding to the product data keyword, the second keyword vector corresponding to the sentiment data keyword, and the third keyword vector corresponding to the service data keyword through a preset word vector model; and obtaining the feature vector corresponding to the green consumption behavior sample data based on the first keyword vector, the second keyword vector and the third keyword vector.

[0106] It should be noted that the TF-IDF matrix can be used as an indicator to measure the importance of each word in the word segmentation result. In practical applications, the TF-IDF matrix consists of two parts: TF (Term Frequency) and IDF (Inverse Document Frequency). The term frequency indicates the number of times a word appears in a specific document. Generally speaking, the more frequently a word appears in a document, the greater its contribution to the topic of the document is generally considered; the inverse document frequency reflects the prevalence of a word in the entire corpus. The higher the IDF value, the more unique the word is in the corpus and the more capable it is of distinguishing different documents. In this embodiment, the device can calculate the degree of association between a word and a text category through the TF-IDF matrix to obtain a score value. The higher the score, the higher the category distinction ability. Therefore, by calculating each word in the word segmentation results of the green consumption behavior sample data (including product data, sentiment data and service data), the calculated scores are arranged in descending order, and the words that are in front of a preset number of words in the sorting results are selected as keywords, or words with scores higher than a preset value are selected as keywords, and finally the keywords in the first word segmentation result, the second word segmentation result and the third word segmentation result are extracted to obtain the above-mentioned product data keywords, sentiment data keywords and service data keywords.

[0107] It should be noted that the above-mentioned preset word vector model can be a vector model for mapping keywords to space. In this embodiment, the product data keywords, the sentiment data keywords, and the service data keywords can be vectorized by the preset word vector model to obtain the first keyword vector, the second keyword vector, and the third keyword vector. Then, the device can perform vector splicing and integration on the first keyword vector, the second keyword vector, and the third keyword vector to finally obtain the feature vector corresponding to the green consumption behavior sample data.

[0108] Step S05: training the initial text classification model using the feature vector to obtain a preset text classification model.

[0109] In practical applications, the above-mentioned initial text classification model can be a deep neural network model, which can be divided into an input layer, a first hidden layer, a second hidden layer and an output layer, wherein the input of the input layer is a feature vector corresponding to the green consumer behavior sample data, the output layer is the probability of the type of the green consumer behavior sample data, and its activation function is a logistics function. After obtaining the feature vector corresponding to the green consumer behavior sample data, this embodiment can input the feature vector into the initial text classification model for training, and when the accuracy of the classification result output by the initial text classification model meets the preset requirements, the above-mentioned preset text classification model is obtained.

[0110] In this embodiment, it is disclosed that green consumption behavior sample data is collected from a web page by a web crawler, and the green consumption behavior sample data includes product data, sentiment data, and service data of the web page; sample identification is configured for the green consumption behavior sample data, and the sample identification is used to characterize the data category of the green consumption behavior sample data; word segmentation is performed on the green consumption behavior sample data to obtain a first word segmentation result corresponding to the product data, a second word segmentation result corresponding to the sentiment data, and a third word segmentation result corresponding to the service data; a feature vector corresponding to the green consumption behavior sample data is obtained based on the first word segmentation result, the second word segmentation result, and the third word segmentation result; an initial text classification model is trained through the feature vector to obtain a preset text classification model, so that the user consumption behavior data can be accurately classified through the preset text classification model, thereby improving the accuracy of the green consumption behavior analysis results.

[0111] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the green consumer behavior analysis method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0112] This application also provides a green consumption behavior analysis device, please refer to Figure 7 , the green consumption behavior analysis device comprises:

[0113] The behavior data acquisition module 10 is used to acquire user consumption behavior data of a number of green consumption products from the green consumption behavior database;

[0114] An economic data determination module 20, configured to determine the platform economic data of the target platform according to the platform survey information and platform economic measurement indicators of the target platform;

[0115] A transmission mechanism construction module 30, for constructing a target two-way feedback transmission mechanism based on the user consumption behavior data and the platform economy data through a simultaneous equation model, wherein the target two-way feedback transmission mechanism is a two-way feedback transmission mechanism between the platform economy and the user's green consumption behavior under the guidance of the platform economy;

[0116] The consumption behavior analysis module 40 is used to simulate and analyze the target two-way feedback transmission mechanism to obtain green consumption behavior analysis results.

[0117] The green consumer behavior analysis device provided by the present application adopts the green consumer behavior analysis method in the above embodiment, which can solve the technical problem that the platform economy in the prior art usually only focuses on the business model and ignores consumer behavior, and fails to fully consider the limited rationality and adaptability of consumer subject behavior to study the social dynamics and complexity of green consumer behavior. Compared with the prior art, the beneficial effects of the green consumer behavior analysis device provided by the present application are the same as the beneficial effects of the green consumer behavior analysis method provided by the above embodiment, and the other technical features in the green consumer behavior analysis device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0118] The present application provides a green consumer behavior analysis device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the green consumer behavior analysis method in the above-mentioned embodiment one.

[0119] Reference below Figure 8 , which shows a schematic diagram of the structure of a green consumer behavior analysis device suitable for implementing the embodiment of the present application. The green consumer behavior analysis device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 8 The green consumption behavior analysis device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0120] like Figure 8As shown, the green consumer behavior analysis device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the green consumer behavior analysis device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the green consumer behavior analysis device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a green consumer behavior analysis device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided alternatively.

[0121] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0122] The green consumer behavior analysis device provided by the present application adopts the green consumer behavior analysis method in the above embodiment, which can solve the technical problems of green consumer behavior analysis. Compared with the prior art, the beneficial effects of the green consumer behavior analysis device provided by the present application are the same as the beneficial effects of the green consumer behavior analysis method provided by the above embodiment, and the other technical features in the green consumer behavior analysis device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0123] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0124] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0125] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the green consumption behavior analysis method in the above-mentioned embodiment.

[0126] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0127] The computer-readable storage medium may be included in the green consumer behavior analysis device; or may exist independently without being assembled into the green consumer behavior analysis device.

[0128] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the green consumption behavior analysis device, the green consumption behavior analysis device: obtains user consumption behavior data of several green consumption products from the green consumption behavior database; determines the platform economic data of the target platform according to the platform survey information and platform economic measurement indicators of the target platform; constructs a target two-way feedback transmission mechanism based on the user consumption behavior data and the platform economic data through a simultaneous equation model, and the target two-way feedback transmission mechanism is a two-way feedback transmission mechanism between the platform economy and the user's green consumption behavior under the dominance of the platform economy; simulates and analyzes the target two-way feedback transmission mechanism to obtain green consumption behavior analysis results.

[0129] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0130] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0131] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0132] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned green consumer behavior analysis method, and can solve the technical problems in the prior art that the platform economy usually only focuses on the business model and ignores consumer behavior, and fails to fully consider the limited rationality and adaptability of consumer subject behavior to study the social dynamics and complexity of green consumer behavior. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the green consumer behavior analysis method provided by the above-mentioned embodiment, which will not be repeated here.

[0133] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A green consumption behavior analysis method, characterized in that: The method includes: Obtaining user consumption behavior data of a number of green consumption products from a green consumption behavior database; Determine the platform economic data of the target platform based on the platform research information and platform economic measurement indicators of the target platform; Constructing a target two-way feedback transmission mechanism based on the user consumption behavior data and the platform economy data through a simultaneous equation model, wherein the target two-way feedback transmission mechanism is a two-way feedback transmission mechanism between the platform economy and the user's green consumption behavior under the guidance of the platform economy; The target two-way feedback transmission mechanism is simulated and analyzed to obtain the analysis results of green consumption behavior.

2. The method according to claim 1, characterized in that Before the step of acquiring the user consumption behavior data of a plurality of green consumption products from the green consumption behavior database, the method further includes: Determine the data collection scope and data collection target, wherein the data collection target includes: green product opinion data and sentiment tendency data based on user feedback on a number of green consumer products; Collecting the green product opinion data and the sentiment tendency data within the data collection range by a preset data collector; A green consumption behavior database is constructed based on the green product opinion data and the sentiment tendency data.

3. The method according to claim 1, characterized in that The step of constructing a target two-way feedback transmission mechanism based on the user consumption behavior data and the platform economic data through a simultaneous equation model includes: Classify the user consumption behavior data through a preset text classification model to obtain green product attribute data, green emotion attribute data and green service attribute data corresponding to each green consumer product; Acquire infrastructure assessment data, information assessment data and platform transaction data of the target platform based on the platform economic data; A target two-way feedback transmission mechanism is constructed through a simultaneous equation model based on the green product attribute data, the green emotional attribute data, the green service attribute data, the infrastructure evaluation data, the information evaluation data and the platform transaction data.

4. The method according to claim 3, characterized in that The step of constructing a target two-way feedback transmission mechanism based on the green product attribute data, the green emotion attribute data, the green service attribute data, the infrastructure evaluation data, the information evaluation data and the platform transaction data through a simultaneous equation model includes: Determine, by a simultaneous equation model, first impact feedback corresponding to the green product attribute data, the green sentiment attribute data, and the green service attribute data on the infrastructure evaluation data, the informationization evaluation data, and the platform transaction data; Determine, by means of the simultaneous equation model, second impact feedback corresponding to the infrastructure assessment data, the information assessment data and the platform transaction data on the green product attribute data, the green emotion attribute data and the green service attribute data; A target bidirectional feedback transmission mechanism is constructed based on the first impact feedback and the second impact feedback.

5. The method according to any one of claims 1 to 4, characterized in that Before the step of acquiring the user consumption behavior data of a plurality of green consumption products from the green consumption behavior database, the method further includes: Collecting green consumption behavior sample data from a web page by using a web crawler, wherein the green consumption behavior sample data includes product data, sentiment data, and service data of the web page; Performing sample identification configuration on the green consumption behavior sample data, wherein the sample identification is used to characterize the data category of the green consumption behavior sample data; Performing word segmentation processing on the green consumption behavior sample data to obtain a first word segmentation result corresponding to the product data, a second word segmentation result corresponding to the sentiment data, and a third word segmentation result corresponding to the service data; Obtaining a feature vector corresponding to the green consumption behavior sample data based on the first word segmentation result, the second word segmentation result, and the third word segmentation result; The initial text classification model is trained using the feature vector to obtain a preset text classification model.

6. The method according to claim 5, characterized in that The step of obtaining the feature vector corresponding to the green consumption behavior sample data based on the first word segmentation result, the second word segmentation result and the third word segmentation result includes: Perform keyword extraction on the first word segmentation result, the second word segmentation result, and the third word segmentation result respectively through the TF-IDF matrix to obtain product data keywords, sentiment data keywords, and service data keywords; Obtaining a first keyword vector corresponding to the product data keyword, a second keyword vector corresponding to the emotion data keyword, and a third keyword vector corresponding to the service data keyword through a preset word vector model; A feature vector corresponding to the green consumption behavior sample data is obtained based on the first keyword vector, the second keyword vector, and the third keyword vector.

7. The method according to any one of claims 1 to 4, characterized in that The step of simulating and analyzing the target two-way feedback transmission mechanism to obtain the green consumption behavior analysis result includes: Performing simulation analysis on the target bidirectional feedback transmission mechanism to obtain model simulation results; Determine the user's green consumption tendency, green consumption decision and green consumption behavior based on the model simulation results; A green consumption behavior analysis result is generated based on the green consumption tendency, the green consumption decision and the green consumption behavior.

8. A green consumption behavior analysis device, characterized in that: The device comprises: A behavior data acquisition module is used to acquire user consumption behavior data of a number of green consumption products from a green consumption behavior database; An economic data determination module, used to determine the platform economic data of the target platform based on the platform research information and platform economic measurement indicators of the target platform; A transmission mechanism construction module, used to construct a target two-way feedback transmission mechanism based on the user consumption behavior data and the platform economy data through a simultaneous equation model, wherein the target two-way feedback transmission mechanism is a two-way feedback transmission mechanism between the platform economy and the user's green consumption behavior under the guidance of the platform economy; The consumption behavior analysis module is used to simulate and analyze the target two-way feedback transmission mechanism to obtain green consumption behavior analysis results.

9. A green consumption behavior analysis device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the green consumption behavior analysis method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the green consumption behavior analysis method according to any one of claims 1 to 7 are implemented.

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