Enterprise-level intelligent product recommendation method based on dynamic industry portrait and multi-dimensional AI capability matching

Abstract

The application provides an enterprise-level intelligent product recommendation method based on dynamic industry portrait and multi-dimensional AI capability matching, relates to the technical field of product recommendation, collects and processes structured data and unstructured data of customers, and obtains customer collection and processing data; industry portrait analysis is performed according to the customer collection and processing data, industry portrait analysis data are obtained, and a dynamic industry portrait is constructed according to the industry portrait analysis data; capability vector analysis is performed on AI products, capability vector analysis data are obtained, the dynamic industry portrait is combined with the capability vector analysis data for matching analysis, and matching recommendation analysis data are obtained; customer feedback data are obtained according to the matching recommendation analysis data, recommendation analysis adjustment is performed according to the customer feedback data, and the matching recommendation analysis data are adjusted until the customer feedback standard is met, so that the application can realize accurate and efficient recommendation of enterprise-level AI products, improve customer satisfaction and reduce enterprise operating costs.

Description

Technical Field

[0001] This invention proposes an enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching, which relates to the field of product recommendation technology, specifically to the field of enterprise-level intelligent product recommendation technology based on dynamic industry profiling and multi-dimensional AI capability matching. Background Technology

[0002] Traditional recommendation methods suffer from limited and unstandardized data collection, often focusing solely on structured data while neglecting the value of unstructured data. This results in low data utilization and incomplete demand identification. Furthermore, industry profiles are typically static, lacking layered design and dynamic update mechanisms. This makes them ill-suited to changes in customer business and industry trends, and the weighting of various dimensions is often unreasonable, failing to highlight key needs. In addition, the product matching logic is simplistic, failing to quantitatively match AI product capabilities with customer needs. This can lead to blind or ineffective recommendations, and the lack of a closed-loop feedback optimization mechanism prevents continuous improvement in recommendation effectiveness. This not only increases enterprise labor and communication costs but also reduces customer satisfaction, making it difficult to meet the current promotion and application needs of enterprise-level AI products. Summary of the Invention

[0003] This invention provides an enterprise-level intelligent product recommendation method based on dynamic industry profiles and multi-dimensional AI capability matching to solve the above-mentioned problems: The present invention proposes an enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching, the method comprising: S1. Collect and process customer structured and unstructured data to obtain customer-collected and processed data; S2. Perform industry profile analysis based on customer-collected and processed data to obtain industry profile analysis data, and construct dynamic industry profiles based on the industry profile analysis data. S3. Perform capability vector analysis on AI products to obtain capability vector analysis data. Combine dynamic industry profiles with capability vector analysis data for matching analysis to obtain matching recommendation analysis data. S4. Obtain customer feedback data based on the matching recommendation analysis data, and adjust the recommendation analysis based on the customer feedback data until the matching recommendation analysis data meets the customer feedback criteria.

[0004] Furthermore, the system includes: The data acquisition and processing module is used to collect and process customer structured and unstructured data to obtain customer-acquired and processed data. The profile building module is used to perform industry profile analysis based on customer collected and processed data, obtain industry profile analysis data, and build dynamic industry profiles based on the industry profile analysis data. The capability analysis module is used to perform capability vector analysis on AI products, obtain capability vector analysis data, and combine dynamic industry profiles with capability vector analysis data for matching analysis to obtain matching recommendation analysis data. The matching and recommendation module is used to obtain customer feedback data based on the matching and recommendation analysis data, and to adjust the recommendation analysis based on the customer feedback data until the matching and recommendation analysis data meets the customer feedback criteria.

[0005] The beneficial effects of this invention are as follows: When applied to a company providing multiple AI solutions, the system automatically identifies the industry of a new customer upon registration and recommends the most suitable combination of AI products accordingly. For example, in the manufacturing sector, predictive maintenance software is recommended for equipment maintenance needs; for the retail sector, inventory management and customer analytics tools are recommended. This improves the accuracy and relevance of recommendations and reduces unnecessary human intervention. It enhances the user experience because the recommended products are closer to actual needs. It promotes increased sales conversion rates, indirectly increasing revenue by improving the success rate of initial contact. The transparent recommendation process, providing detailed reasons for recommendations, helps enhance customer trust. Attached Figure Description

[0006] Figure 1 This is a schematic diagram of an enterprise-level intelligent product recommendation method based on dynamic industry profiles and multi-dimensional AI capabilities. Detailed Implementation

[0007] 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.

[0008] In one embodiment of the present invention, an enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching is proposed, the method comprising: S1. Collect and process customer structured and unstructured data to obtain customer-collected and processed data; It collects structured customer data (basic enterprise information, business indicators, etc.) and unstructured data (interview records, industry news, etc.), and after cleaning and standardization, generates usable customer data collection and processing data.

[0009] S2. Perform industry profile analysis based on customer-collected and processed data to obtain industry profile analysis data, and construct dynamic industry profiles based on the industry profile analysis data. S3. Perform capability vector analysis on AI products to obtain capability vector analysis data. Combine dynamic industry profiles with capability vector analysis data for matching analysis to obtain matching recommendation analysis data. S4. Obtain customer feedback data based on the matching recommendation analysis data, and adjust the recommendation analysis based on the customer feedback data until the matching recommendation analysis data meets the customer feedback criteria, such as... Figure 1 As shown.

[0010] The working principle and technical effects of the above solution are as follows: Comprehensive collection of structured and unstructured customer data, followed by cleaning and standardization to eliminate data clutter and inconsistent formats, addressing the limitations of traditional recommendation methods such as single data sources and low data quality; industry profiling analysis of the processed customer data to extract multi-dimensional industry characteristics, constructing hierarchical and dynamic industry profiles, accurately capturing the characteristics of the customer's industry, their own needs, and pain points, overcoming the limitations of traditional static profiles that cannot adapt to changing customer needs; multi-dimensional capability vector analysis of AI products, quantifying the capabilities of AI products into computable and matchable vector data, and performing bidirectional matching analysis with the constructed industry profiles, using computational capability differences to achieve ranking and recommendation of AI products, solving the problems of single matching logic and insufficient accuracy in traditional recommendation methods; collection of customer feedback, dynamically adjusting recommendation parameters and updating recommendation data based on feedback results, ensuring that the recommendation scheme can continuously adapt to changes in customer needs and guaranteeing the long-term stability of recommendation effects. The entire process is progressive and interconnected, with the output of each step serving as the input for the next, achieving full automation and intelligence, minimizing manual intervention, and improving recommendation efficiency and accuracy.

[0011] This method addresses the issues of non-standardized data processing and low data utilization in traditional recommendation methods. Through comprehensive data collection and standardized processing, it integrates structured and unstructured customer data, achieving unified management and efficient utilization of customer data and avoiding the problem of incomplete demand mining caused by single data types. It also solves the problem of traditional recommendation methods lacking accurate industry profiles and being unable to adapt to differentiated customer needs. By constructing dynamic industry profiles, it can accurately capture the characteristics of the customer's industry, the customer's own business features, and core needs, breaking the limitations of static profiles and enabling synchronous updates of profiles with customer needs and industry changes, making recommendation solutions more targeted. Furthermore, it addresses the problems of simplistic matching logic and low recommendation accuracy in traditional recommendation methods. Through multi-dimensional AI capability vector analysis and bidirectional matching, it accurately matches AI product capabilities with customer needs, avoiding blind and ineffective recommendations, improving the rationality and adaptability of recommendation solutions, and making recommended AI products more aligned with the actual business needs of customers. This solution addresses the problem of traditional recommendation methods lacking feedback optimization mechanisms and failing to continuously improve recommendation effectiveness. Through closed-loop feedback adjustment, it can collect customer feedback in a timely manner, dynamically optimize recommendation parameters and schemes, ensure that recommendation effectiveness can continuously adapt to changes in customer needs, and improve customer recognition and satisfaction with the recommendation scheme.

[0012] In one embodiment of the present invention, S1 includes: all collected data is authorized.

[0013] The customer's structured data is collected using a preset structured data collection method to obtain the first set of collected data; We collect structured customer data by using API integration, form entry, and third-party data supplementation, and then organize it to obtain the first set of collected data.

[0014] The second set of data is obtained by collecting unstructured customer data through a preset unstructured data collection method. We use web crawlers, authorized access, and manual data entry to collect unstructured text and documents from clients, and then process them to obtain the second set of collected data.

[0015] Based on the first set of collected data, a data acquisition framework is constructed to obtain data acquisition framework construction information; Based on the first dimension of collected data, a data collection framework is built, the framework structure and field requirements are defined, and the collection framework construction information is generated.

[0016] Perform frame filling analysis on the data collection framework construction information to obtain frame filling analysis data; analyze the blank fields of the data collection framework to determine the filling requirements and generate frame filling analysis data.

[0017] Based on the frame filling analysis data, fill feature extraction is performed on the second collected data to obtain frame filling extracted data; based on the frame filling analysis data, the required features for the corresponding blank fields are extracted from the second collected data to obtain frame filling extracted data.

[0018] The extracted data is used to populate the data collection framework construction information to obtain customer data collection and processing data. This extracted data is then filled into the corresponding blank fields of the data collection framework to complete the population process and obtain the customer data collection and processing data.

[0019] The working principle and technical effects of the above technical solution are as follows: Through standardized and multi-channel collection methods, it comprehensively acquires the client's structured and unstructured data. Then, through systematic processing, it transforms the messy and scattered data into standardized and usable client-collected data, while strictly ensuring the legality of data collection. Specifically, the principles are as follows: The legality of data collection is clearly defined; all collected data is authorized by the client to prevent data leakage and unauthorized use, ensuring client data security. Different collection methods are adopted for structured and unstructured data. Structured data is collected through interface integration (such as integration with the client's enterprise management system), form entry (such as basic information forms filled out by the client), and third-party data supplementation (such as legal and compliant industry databases), ensuring the standardization and accuracy of the data and forming the initial collected data. Unstructured data is collected through the network... Data is collected through various methods, including web scraping (from client company websites and industry-related reports), authorized access (from client-authorized business documents and interview records), and manual data entry (from offline communication records with clients), ensuring data comprehensiveness and forming the second set of collected data. Based on the dimensions of the first set of collected data, a data collection framework is built, clarifying the framework's structure, field requirements, and data types, generating framework construction information. The blank fields of the collection framework are analyzed to clarify the filling requirements for each blank field, generating framework filling analysis data. Then, based on the framework filling analysis data, the feature information required for the corresponding blank fields is extracted from the second set of collected data to obtain framework filling extracted data. The framework filling extracted data is then filled into the corresponding blank fields of the collection framework to complete data filling, forming standardized and complete client data collection and processing data, ensuring that the data can be directly used for subsequent industry profile construction.

[0020] This method addresses the issue of data collection legality. By ensuring that all data is authorized by the client, it avoids risks such as unauthorized collection and data leakage, guaranteeing client data security, complying with data compliance requirements, and reducing the enterprise's data compliance risks. It also solves the problems of traditional data collection methods being singular and lacking comprehensive data coverage. Through differentiated collection methods, it simultaneously collects structured and unstructured data, comprehensively covering various aspects of client information, including basic information, business indicators, business pain points, and industry dynamics. This avoids the problem of incomplete demand mining caused by single data types and improves data comprehensiveness. Furthermore, it addresses the issue of messy and unusable data formats. By building a collection framework, data filling, and standardization processing, it transforms scattered and messy raw data into standardized, structured client-collected and processed data, eliminating problems such as inconsistent data formats, redundancy, and missing data. This improves data usability and provides high-quality data support for subsequent industry profile construction and product matching, reducing data processing costs in subsequent stages. This approach addresses the disconnect between data collection and subsequent processes. By building a collection framework based on structured data and then filling in blank fields with unstructured data, it enables the synergistic use of both types of data. This ensures that the collected data accurately matches the needs of subsequent industry profile construction, improving the coherence and efficiency of the entire recommendation process. It also reduces the workload of manual data processing. Through standardized collection and processing procedures, it achieves semi-automation of data collection, processing, and data filling, minimizing errors caused by human intervention and improving the efficiency and accuracy of data processing.

[0021] In one embodiment of the present invention, the step of performing frame filling analysis on the collected frame construction information to obtain frame filling analysis data includes: Based on the information collected, the framework feature information is determined, and the list of blank data in the framework is determined based on the framework feature information. The features and fields of the collection framework are parsed, the unfilled blank fields are filtered out, and the list of blank data in the framework is compiled.

[0022] The correlation between the first and second collected data is calculated based on the blank data list in the framework to obtain the data correlation of the list; the correlation between the first and second collected data corresponding to the blank fields is calculated using the cosine similarity algorithm to obtain the data correlation of the list.

[0023] The correlation degree of the list data is compared with the preset list data correlation threshold to obtain list correlation comparison information; the preset correlation threshold (e.g., 0.6) is used to compare the list data correlation degree with the threshold, and the comparison results are recorded to obtain list correlation comparison information.

[0024] Based on the comparison information of the list, determine the unstructured data type that needs to be filled in each blank field; based on the comparison results, determine the unstructured data type (such as text, document fragment) that each blank field is suitable for.

[0025] Based on the list's correlation and comparison information, frame filling priorities are set, blank fields are prioritized, and unstructured data corresponding to high-priority blank fields are filled first, generating frame filling analysis data. Filling priorities are also set according to their correlation, with blank fields of high correlation being filled first. All information is then integrated to generate frame filling analysis data.

[0026] The working principle and technical effect of the above technical solution are as follows: This method analyzes the core features and field structure of the information constructed by the collection framework, clarifies the filled fields and unfilled blank fields in the framework, filters out all blank fields and organizes them into a blank data list of the framework, clearly presenting the content that needs to be filled; based on the blank data list of the framework, for each blank field, the cosine similarity algorithm is used to calculate the correlation between the first collected data (structured data) and the second collected data (unstructured data) corresponding to the blank field, that is, to determine which content in the unstructured data has the strongest correlation with the blank field, and obtain the correlation of the list data; a reasonable list data correlation threshold is preset (such as 0.6, which can be flexibly adjusted according to industry characteristics), and the calculated list data correlation is compared with the preset threshold, recorded. The process involves recording whether the relevance of each blank field reaches a threshold, creating a list of relevance comparison information to determine whether unstructured data is suitable for filling the blank field. Based on this information, the required unstructured data type for each blank field is determined. For example, if a blank field with a relevance threshold corresponds to a business pain point in the structured data, the unstructured data type to be filled is determined to be interview records or text fragments from business documents. The framework filling priority is set based on the relevance comparison information. Blank fields with higher relevance have a greater impact on subsequent industry profile building and product matching, thus requiring higher priority. Unstructured data corresponding to these blank fields is filled first. All information, including the blank field list, relevance comparison results, filling type, and filling priority, is integrated to generate framework filling analysis data.

[0027] This method addresses the issue of unclear direction in filling blank fields within a framework. By analyzing framework features and compiling a list of blank data, it clearly defines the content to be filled, avoiding blindly extracting unstructured data for filling and improving the targeting of data filling. It also solves the problem of low matching between unstructured data and blank fields. By calculating data correlation and comparing it with a preset threshold, it ensures that the extracted unstructured data is highly correlated with the blank fields, avoiding invalid or erroneous filling and improving the accuracy of data filling, thereby enhancing the quality of data collected and processed by the client. Furthermore, it addresses the problem of low data filling efficiency. By setting filling priorities, it prioritizes filling blank fields with high correlation and significant impact, rationally allocating data processing resources and avoiding inefficiency caused by unprioritized filling, thus improving data filling efficiency and shortening the data processing cycle. Finally, it resolves the problem of data filling being disconnected from the needs of subsequent stages. By clarifying the filling type and priority of each blank field, it ensures that the filled data better adapts to the needs of subsequent industry profile construction, improving the coherence and accuracy of the entire recommendation process.

[0028] In one embodiment of the present invention, S2 includes: Based on a pre-set industry knowledge base, multi-dimensional industry features are extracted from customer-collected and processed data to obtain industry profile extraction data; by calling the pre-set industry knowledge base and using NLP technology, industry-related features are extracted from customer-collected and processed data to obtain industry profile extraction data.

[0029] Industry profile extraction data is subjected to industry profile layer analysis to obtain industry profile layer analysis data; dynamic industry profile extraction data is divided into basic layer, feature layer and demand layer, and the data and relationships of each layer are analyzed to obtain industry profile layer analysis data.

[0030] Based on the industry profile stratification analysis data, profile dimension weight analysis is performed to obtain profile dimension weight data; using the analytic hierarchy process (AHP) and combined with industry demand standards, the weight of each stratified dimension is calculated to obtain profile dimension weight data.

[0031] Industry profiles are constructed by combining industry profile layering analysis data with profile dimension weight analysis data to obtain industry profile construction data; by combining layered data and weight data, a profile model is built, data is integrated, and standardized processing is performed to obtain industry profile construction data.

[0032] Validate and update the industry profile data to obtain dynamic industry profiles. Verify the accuracy of the profile data (accuracy ≥ 95%), update the data regularly or as needed, and generate dynamic industry profiles.

[0033] The working principle and technical effects of the above technical solution are as follows: A pre-set industry knowledge base is invoked (this knowledge base contains basic information, development trends, business pain points, and demand characteristics of various industries, and can be updated regularly). Natural Language Processing (NLP) technology is used to extract multi-dimensional industry-related features from customer-collected and processed data, including basic industry features, customer-specific features, and business pain point features, to obtain industry profile extraction data. This industry profile extraction data is then subjected to hierarchical analysis, dividing it into three levels: a basic layer, a feature layer, and a demand layer. Relevant data is extracted from each level, and the relationships between the data at each level are analyzed to obtain hierarchical industry profile analysis data, making the profile structure clearer and more targeted. The Analytic Hierarchy Process (AHP) is then employed to... Based on pre-defined industry demand weight standards, the weights of each layer and data dimension are determined, prioritizing dimensions that have a greater impact on recommendation results (such as business pain points in the customer demand layer), thus obtaining profile dimension weight data. Combining industry profile layer analysis data and profile dimension weight data, a basic industry profile model is built, importing data from each layer and establishing correlation mapping relationships, assigning weights to each dimension, merging data from each layer and eliminating redundancy and conflicts, and performing standardization processing to obtain industry profile construction data. The accuracy of the industry profile construction data is verified to ensure that the profile can truly reflect the customer's industry characteristics and needs. At the same time, a dynamic update mechanism is established to update the profile data regularly or as needed (such as when there are significant changes in the customer's business), ultimately obtaining a dynamic industry profile.

[0034] This method addresses the issue of incomplete feature extraction in traditional industry profiling. By leveraging industry knowledge bases and NLP techniques, it extracts multi-dimensional industry features from customer-collected and processed data, covering basic information, industry characteristics, and pain points. This avoids the one-sidedness of profiling caused by single feature extraction, improving the comprehensiveness of the profiling. It also solves the problems of disorganized and untargeted structures in traditional industry profiling. Through hierarchical analysis, the profiling is divided into a basic layer, a feature layer, and a demand layer, clearly defining the core content and relationships of each layer. This makes the profiling structure clearer and more logical, accurately capturing the core needs of customers and improving the precision of the profiling. Finally, it addresses the problem of unreasonable weighting of dimensions in traditional industry profiling. By combining the analytic hierarchy process (AHP) with industry demand standards, it scientifically allocates weights to each dimension, prioritizing core needs and key features. This avoids the lack of emphasis caused by equal weighting of dimensions, improving the accuracy of the profiling. This solution addresses the limitations of static industry profiles, which fail to adapt to changing needs. By establishing a dynamic update mechanism, profile data is updated regularly or on demand, ensuring that profiles keep pace with changes in customer business and industry trends. This breaks the limitations of static profiles and improves their timeliness and adaptability. Furthermore, it resolves the disconnect between industry profiles and subsequent product matching. Through standardized processing of profile data, it ensures that profile data can be directly used for matching subsequent AI product capabilities, improving the coherence and efficiency of the entire recommendation process and reducing the probability of ineffective recommendations.

[0035] In one embodiment of the present invention, the step of performing industry profile layering analysis on the extracted industry profile data to obtain industry profile layering analysis data includes: The industry data is extracted into three layers: basic information, feature information, and demand information. The basic information, feature information, and demand information are extracted from the data. The data is then extracted from the industry profile data. The basic information (enterprise and industry basic information), feature information (industry and customer features), and demand information (pain points and potential needs) are extracted respectively to obtain the corresponding three layers of data.

[0036] The correlation between the base layer data and the feature layer data is calculated to obtain the first correlation data; the Pearson correlation coefficient is used to calculate the correlation between the base layer data and the feature layer data to obtain the first correlation data.

[0037] Calculate the correlation between the feature layer data and the demand layer data to obtain the second correlation data; use the Pearson correlation coefficient to calculate the correlation between the feature layer data and the demand layer data to obtain the second correlation data.

[0038] The correlation between the basic layer data and the demand layer data is calculated to obtain the third correlation data; the Pearson correlation coefficient is used to calculate the correlation between the basic layer data and the demand layer data to obtain the third correlation data.

[0039] Based on the first, second, and third correlation data, the basic layer data, feature layer data, and demand layer data are combined to obtain industry profile layered analysis data. Based on the three layers of correlation data, fields with high correlation are prioritized for combination, and the three layers of data are integrated to obtain the industry profile layered analysis data.

[0040] The working principle and technical effects of the above technical solution are as follows: This method clearly defines the hierarchical standards, dividing the dynamic industry profile extraction data into three levels: the basic layer, the feature layer, and the demand layer. The basic layer mainly carries basic information about the customer and their industry; the feature layer mainly carries differentiated feature information about the industry and the customer; and the demand layer mainly carries the customer's core needs and pain points. According to this hierarchical standard, basic information (such as company name, industry category, company size, etc.), feature information (such as industry development stage, customer core business, industry competitive landscape, etc.), and demand information (such as customer business pain points, potential needs, and existing system shortcomings) are extracted from the industry profile extraction data. The data is processed in three layers: foundational data, feature data, and demand data. The Pearson correlation coefficient algorithm is used to calculate the correlation between the foundational data and feature data (first correlation data), the correlation between feature data and demand data (second correlation data), and the correlation between foundational data and demand data (third correlation data), thus determining the degree of correlation between data at each layer. Based on the calculated three correlation coefficients, the foundational, feature, and demand data are combined, prioritizing the combination of fields with high correlation to ensure the coherence and logic between data at each layer. The resulting integrated data yields industry profile layered analysis data.

[0041] This method addresses the problems of disorganized and unfocused traditional industry profile structures. Through clear hierarchical standards, it divides profile data into three levels, each with clearly defined responsibilities and priorities, avoiding the one-sidedness of profiles caused by chaotic data and improving the structure of the profiles. It also solves the problem of unclear data relationships between levels. By calculating three correlation coefficients, it clearly understands the closeness of the connections between the basic layer, feature layer, and demand layer, avoiding the incoherence of profiles caused by disconnected data and improving the logical consistency of the profiles. Furthermore, it addresses the lack of clear basis for subsequent weight calculations and profile construction. The hierarchical data and correlation coefficients obtained through layered analysis ensure that weight allocation is more aligned with actual needs, while also providing guidance for data fusion and correlation mapping during profile construction, improving the efficiency and accuracy of profile construction. Finally, it addresses the problem of industry profiles failing to accurately capture core customer needs. By separately defining a demand layer, it focuses on extracting customer business pain points and potential needs, making core customer needs more prominent and facilitating accurate matching of customer needs during subsequent product matching, thus improving the accuracy of recommendations. It improves the scalability and maintainability of industry profiles. The hierarchical structure makes profile data management clearer. When updating profile data in the future, updates can be made at specific levels without overall adjustments, which reduces the cost of profile maintenance and improves maintenance efficiency.

[0042] In one embodiment of the present invention, the step of performing profile dimension weight analysis based on industry profile layering analysis data to obtain profile dimension weight data includes: The weighted influencing factors are determined by analyzing the data in a stratified manner based on industry profiles. The weighted influencing factors are determined to be data relevance, urgency of demand, and industry representativeness, which aligns with the goal of recommendation accuracy.

[0043] The weights of each dimension of the industry profile stratified analysis data are calculated using the analytic hierarchy process (AHP) to obtain dimensional weight calculation data. The AHP is then used to construct a judgment matrix and calculate the initial weights of each stratum and dimension to obtain dimensional weight calculation data.

[0044] Based on the preset industry demand weight standards, the dimensional weight calculation data is adjusted to obtain weight adjustment data; the initial weights are corrected by comparing them with the preset industry demand weight standards (such as increasing the weight of pain points in equipment maintenance in the manufacturing industry) to obtain weight adjustment data.

[0045] The weight adjustment data is normalized to obtain the portrait dimension weight data. The weight adjustment data is then normalized to ensure that the sum of all dimension weights is 1, thus obtaining the portrait dimension weight data.

[0046] The working principle and technical effects of the above technical solution are as follows: This method analyzes data in layers based on industry profiles, combines this with the core objective of recommendation accuracy, determines the weighting factors, and clarifies the key factors affecting weight allocation. These factors specifically include data relevance (the degree of relevance between data and AI products), demand urgency (the urgency of customer needs), and industry representativeness (the degree to which data reflects industry characteristics). These three factors directly determine the degree of influence of each dimension of data on the recommendation results. Using the Analytic Hierarchy Process (AHP), a judgment matrix is ​​constructed, and each layer and each data dimension is compared pairwise according to the weighting factors to determine the relative importance of each dimension and calculate the initial weights for each dimension. The initial weights are calculated and then adjusted against pre-defined industry demand weight standards (which are established in advance based on the characteristics of different industries and can be updated periodically). For example, the weight of the equipment maintenance pain point dimension for manufacturing customers needs to be increased, and the weight of the inventory management demand dimension for retail customers needs to be increased. This corrects any mismatch between the initial weights and industry demands, resulting in adjusted weight data. The adjusted weight data is then normalized by converting the weight values ​​of all dimensions into values ​​between 0 and 1 through mathematical calculations, ensuring that the sum of all dimension weights is 1. This forms standardized profile dimension weight data, clarifying the importance of each dimension in the industry profile.

[0047] This method addresses the lack of scientific basis in traditional weight allocation. By clearly defining the influencing factors of weights and combining them with the analytic hierarchy process (AHP) for weight calculation, it ensures the scientific and rational nature of weight allocation, avoiding biases caused by subjective manual weight assignment and improving accuracy. It also solves the problem of equal weight across dimensions and a lack of emphasis on key aspects. Through weight adjustment and normalization, it prioritizes increasing the weight of core needs and key features while decreasing the weight of secondary information, enabling industry profiles to highlight core customer needs and key industry characteristics, thus improving targeting and accuracy. Furthermore, it addresses the disconnect between weight allocation and industry needs. By adjusting initial weights against preset industry demand weight standards, it ensures that weight allocation adapts to the characteristics of different industries, avoiding recommendation biases caused by uniform weight standards and improving the industry adaptability of industry profiles. Finally, it clarifies the importance of data in each dimension, allowing subsequent AI product capabilities to prioritize matching needs in high-weight dimensions, improving product matching accuracy, reducing invalid recommendations, and increasing customer satisfaction. It improves the standardization and repeatability of weight allocation. Through standardized weight calculation, adjustment and normalization processes, it ensures that the weight allocation process is consistent for different customers and industries, which facilitates weight adjustment and optimization and reduces the workload and error of manual intervention.

[0048] In one embodiment of the present invention, the step of constructing industry profiles based on industry profile hierarchical analysis data and profile dimension weight analysis data to obtain industry profile construction data includes: A basic industry profile model is built based on industry profile layered analysis data and profile dimension weight analysis data; a basic industry profile model is built based on the TensorFlow framework with a three-layer layered structure, and data interfaces are reserved.

[0049] Using the three-layer structure of industry profile hierarchical analysis data as a framework, all analysis data from the basic layer, feature layer, and demand layer are imported to establish the correlation mapping relationship between the data of each layer; the three layers of analysis data are imported into the model, and the correlation mapping relationship between the basic layer and the feature layer, and between the feature layer and the demand layer is established using a key-value pair association method.

[0050] Based on the portrait dimension weight data, weight values ​​are assigned to each layer of data dimensions to increase the proportion of high-weight dimension data and decrease the proportion of low-weight dimension data. Weight data is assigned to each dimension with a preset weight threshold (0.15). High-weight (≥0.15) dimension data is displayed first, and low-weight dimensions are used as a supplement.

[0051] The data from each layer is fused to eliminate data redundancy and conflicts, resulting in fused profile data. First, intra-layer data is fused to remove redundancy, then cross-layer data is fused. Conflicting data is processed according to the principle of prioritizing data from the highest-weighted source, resulting in fused profile data.

[0052] The fused profile data is standardized and converted into a structured format that matches the capabilities of AI products to generate industry profile building data. The fused data is converted into a JSON structured format, with feature types and weights labeled, generating industry profile building data containing complete data and relationships.

[0053] The working principle and technical effects of the above technical solution are as follows: This method is based on the TensorFlow framework and uses the three-layer structure (basic layer, feature layer, and demand layer) of industry profile hierarchical analysis data as a foundation to build a basic industry profile model. Data import and association mapping interfaces are reserved to ensure that the model can support data at each level and achieve data association. Using the three-layer structure as a framework, all data from the basic, feature, and demand layers of dynamic industry profile hierarchical analysis data are imported into the model. Key-value pair associations are used to establish association mapping relationships between the basic layer and the feature layer, and between the feature layer and the demand layer, ensuring that data at each level can be interconnected and consistent. Based on the profile dimension weight data, weights are assigned to each dimension of the data in each layer, and the weight data is written into the corresponding dimension's attribute. A preset weight threshold (e.g., 0.15) is used, ensuring that weights ≥ 0. Data with 15 dimensions is used as the core feature and is displayed first. Data with a weight of less than 0.15 is used as auxiliary features to supplement and improve the profile. The data of each layer is fused. First, data within the layer is fused to eliminate redundancy (such as unifying synonyms). Then, cross-layer data is fused. The data of each layer is integrated by combining the correlation mapping relationship. For data conflicts, the principle of prioritizing the source of the higher weight data is followed to obtain the fused profile data. The fused profile data is standardized and converted into JSON structured format (to facilitate subsequent matching with AI product capability vector data). The feature type (core feature / auxiliary feature) and weight value of each dimension are labeled. The complete profile data, correlation relationship, weight allocation and other information are integrated to generate industry profile construction data. This ensures that the data can be directly used for AI product capability matching in the subsequent S3 stage.

[0054] This method addresses the issues of traditional industry profiling models being non-standardized and unable to support multi-dimensional data. By building a standardized profiling foundation model based on the TensorFlow framework, it effectively supports three layers of hierarchical data, achieving orderly data management and relational mapping, thus improving the standardization and efficiency of profiling construction. It resolves the problems of disconnected and inconsistent data across different layers by establishing relational mapping relationships between the data at each layer, achieving an organic combination of the foundational, feature, and demand layers. This ensures consistent and unified profiling data, comprehensively and accurately reflecting the industry characteristics and needs of customers, thus improving the completeness of the profiling. It also resolves the issues of data redundancy and conflict by eliminating data redundancy and synonyms through intra-layer and cross-layer data fusion processing. Data conflicts are resolved according to the principle of prioritizing high-weight sources, ensuring the accuracy and consistency of profiling data and improving its quality. Finally, it addresses the issue of industry profiling not being directly usable for product matching by standardizing the fused data into a JSON structured format, labeling feature types and weights, ensuring that the profiling data is consistent with the AI ​​product capability vector data format, and can be directly used for matching analysis. This improves the consistency and efficiency of the entire recommendation process and reduces data processing costs in subsequent stages. This solves the problem of insufficient focus in industry profiles. By assigning weights and dividing core and auxiliary features, it makes the core needs and key characteristics of customers more prominent, which facilitates accurate product matching and improves the accuracy of recommendations. It also makes it easier for the sales team to quickly grasp the core needs of customers.

[0055] In one embodiment of the present invention, S3 includes: Obtain preset capability analysis indicator information, analyze the AI ​​product capability indicators based on the preset capability analysis indicator information, and obtain AI product capability indicator analysis data; obtain preset capability analysis indicators (functional adaptation, pain point resolution, etc.), analyze the indicator compliance status of each AI product, and obtain AI product capability indicator analysis data.

[0056] Based on the AI ​​product capability index analysis data, generate AI product capability index vector data to obtain product capability vector analysis data; quantify the AI ​​product capability index analysis data to generate multi-dimensional capability vectors to obtain product capability vector analysis data.

[0057] Based on the preset capability analysis indicators, the industry profile is analyzed to obtain the capability indicator analysis data; by comparing with the preset capability indicators, the corresponding demand indicators of the industry profile are analyzed to obtain the capability indicator analysis data of the industry profile.

[0058] Based on the industry profile capability index analysis data, generate industry profile capability index vector data to obtain profile capability vector analysis data; quantify the dynamic industry profile capability index analysis data to generate demand capability vectors to obtain profile capability vector analysis data.

[0059] The difference between the product capability vector analysis data and the profile capability vector analysis data of each AI product capability indicator analysis data is obtained to obtain capability gap information; the Euclidean distance algorithm is used to calculate the difference between the capability vector of each AI product and the capability vector of the profile to obtain capability gap information.

[0060] The capability gaps between multiple AI products corresponding to dynamic industry profiles are sorted from largest to smallest to obtain matching recommendation analysis data. The capability gaps of each AI product are then sorted from largest to smallest, with smaller gaps ranking higher. This process yields the matching recommendation analysis data.

[0061] The working principle and technical effects of the above technical solution are as follows: First, it acquires preset capability analysis indicator information. This indicator information covers the core capability dimensions of AI products (such as functional adaptability, pain point resolution capability, cost controllability, and implementation difficulty), serving as a unified standard for measuring AI product capabilities and customer needs. Second, based on the preset capability analysis indicator information, it analyzes the capability indicators of each AI product, clarifying the achievement status and specific performance of each AI product on each capability indicator, thus obtaining AI product capability indicator analysis data. Third, it quantifies the AI ​​product capability indicator analysis data, converting the performance of each capability indicator into numerical values, generating a multi-dimensional AI product capability vector, and obtaining product capability vector analysis data, achieving quantifiable and matchable AI product capabilities. Finally, it compares the results with preset capability indicators. The system analyzes performance indicators to interpret industry profiles and identify customer needs and expectations for each indicator, resulting in industry profile capability indicator analysis data. This dynamic data is then quantified to generate multi-dimensional customer demand capability vectors, providing profile capability vector analysis data and enabling the quantification of customer needs. Next, an Euclidean distance algorithm is used to calculate the difference between the capability vector of each AI product and the customer demand capability vector. This difference represents the capability gap; a smaller difference indicates a higher degree of alignment between the AI ​​product's capabilities and customer needs. All AI products are then sorted from largest to smallest capability gap, with smaller gaps ranking higher. The ranking results are then integrated to obtain matching recommendation analysis data.

[0062] This method addresses the limitation of traditional recommendation methods' simplistic matching logic. Through multi-dimensional capability index analysis and capability vector analysis, it achieves precise two-way matching between AI product capabilities and customer needs, breaking the limitations of traditional single-dimensional matching (such as focusing solely on functionality) and improving the comprehensiveness and accuracy of the matching. It also solves the problem of quantifiable matching between AI product capabilities and customer needs. By converting AI product capabilities and customer needs into calculable capability vectors and using the Euclidean distance algorithm to calculate capability differences, it quantifies the degree of matching, making recommendation ranking more scientific and objective, and avoiding biases caused by subjective human ranking. Furthermore, it addresses the issues of ineffective and blind recommendations. Ranking by capability gaps prioritizes AI products with high relevance to customer needs, reducing recommendations of products that do not match customer requirements, improving recommendation effectiveness, preventing customers from receiving irrelevant information, and enhancing customer experience. Finally, it addresses the problem of low efficiency in sales team interactions with customers. By generating clear matching recommendation analysis data, it clarifies the ranking and relevance of AI products, allowing sales teams to quickly grasp the advantages of each product and its matching points with customer needs, reducing communication costs and improving sales interaction efficiency. It improves the interpretability of the recommendation scheme, makes the process of capability vector analysis and capability gap calculation traceable, clearly explains the basis for recommendation ranking, makes it easier to explain the reasons for recommendations to customers, and enhances customers' trust in the recommendation scheme.

[0063] In one embodiment of the present invention, S4 includes: Personalized recommendation reports are pushed to customers based on matching recommendation analysis data; personalized reports containing product rankings and reasons for recommendation are generated based on matching recommendation analysis data and pushed to customers.

[0064] Feedback information is collected based on the personalized recommendation report to obtain recommendation feedback collection data; customer satisfaction with the recommendation report and modification suggestions are collected through feedback forms and offline interactions to obtain recommendation feedback collection data.

[0065] The collected recommendation feedback data is compared with the preset recommendation feedback threshold to obtain recommendation comparison data; The recommendation status is determined based on the comparison data, resulting in recommendation status determination data. A preset feedback threshold (e.g., satisfaction ≥ 4 points) is set, and the feedback data is compared with the threshold to obtain recommendation comparison data. Based on the comparison results, the recommendation status is determined to be either satisfactory or unsatisfactory, resulting in recommendation status determination data.

[0066] Based on the recommendation status determination data, the matching recommendation analysis data is updated, and then adjusted until it meets the customer feedback criteria. If it does not meet the criteria, the matching weights and product rankings are adjusted based on feedback, and the recommendation analysis data is updated; this feedback-adjustment process is repeated until the customer feedback criteria are met.

[0067] The working principle and technical effects of the above technical solution are as follows: Based on the matching recommendation analysis data, a personalized recommendation report is generated. The report includes the ranking of AI products, the reasons for the recommendation, and the matching points with customer needs. The report is then pushed to the customer, providing them with a clear recommendation reference. Customer feedback information is collected through multiple channels, including online feedback forms (collecting customer satisfaction with the recommendation report, suggestions for modification, etc.) and offline sales feedback (collecting customer questions about the product, adjustments to needs, etc.). All feedback information is integrated to obtain recommendation feedback collection data. A preset recommendation feedback threshold (e.g., customer satisfaction ≥ 4 points, this threshold can be flexibly adjusted according to the company's needs) is set, and the recommendation feedback collection data is compared with the preset threshold. For example, customer satisfaction... The process involves comparing the number of customer-submitted modification suggestions with a threshold to obtain recommendation comparison data. Based on this data, the recommendation status is determined. If the feedback data reaches a preset threshold, the recommendation status is considered satisfactory and requires no adjustment. If it does not reach the preset threshold, the recommendation status is considered unsatisfactory and requires adjustment, resulting in recommendation status determination data. If the recommendation status is unsatisfactory, recommendation parameters are adjusted based on specific customer feedback, such as adjusting the weights of AI product capability vectors, adjusting product sorting order, and supplementing compatible AI products, updating the matching recommendation analysis data. This process is repeated until the matching recommendation analysis data meets the customer feedback standards, forming a closed-loop iteration to ensure the recommendation solution continuously adapts to customer needs.

[0068] This method addresses the limitations of traditional recommendation methods, which offer one-off recommendations and cannot adapt to changing customer needs. Through a closed-loop feedback adjustment mechanism, it can collect customer feedback in a timely manner, dynamically optimize recommendation schemes, and ensure that the schemes continuously adapt to changes in customer needs and business operations, thus improving the adaptability and timeliness of the recommendations. It also solves the problem of unquantifiable recommendation effects and the inability to determine whether customer needs are met. By setting preset feedback thresholds and status judgments, it can clearly determine whether the recommendation scheme meets the standards, avoiding customer dissatisfaction caused by blind recommendations and improving the controllability of recommendation effects. Furthermore, it addresses the issues of low customer satisfaction and low acceptance of recommendation schemes. By collecting customer feedback and making targeted adjustments, it can effectively resolve customer questions and needs, making the recommendation schemes more aligned with actual customer needs, increasing customer acceptance and satisfaction, and enhancing customer trust in the company. Finally, it solves the problem of the inability to continuously optimize recommendation algorithms and parameters. Through feedback adjustment, it continuously optimizes recommendation parameters and adjusts product matching logic, improving the accuracy of the recommendation algorithm and ensuring long-term stable improvement in recommendation effects, preventing a decline in recommendation performance. It has improved the company's service quality and market competitiveness. By continuously optimizing the recommendation scheme, it meets the personalized needs of different customers, reduces ineffective communication and recommendations, improves the company's service efficiency and customer loyalty, and brings the company a better market reputation and competitiveness.

[0069] According to one embodiment of the present invention, the system includes: The data acquisition and processing module is used to collect and process customer structured and unstructured data to obtain customer-acquired and processed data. It collects structured customer data (basic enterprise information, business indicators, etc.) and unstructured data (interview records, industry news, etc.), and after cleaning and standardization, generates usable customer data collection and processing data.

[0070] The profile building module is used to perform industry profile analysis based on customer collected and processed data, obtain industry profile analysis data, and build dynamic industry profiles based on the industry profile analysis data. The capability analysis module is used to perform capability vector analysis on AI products, obtain capability vector analysis data, and combine dynamic industry profiles with capability vector analysis data for matching analysis to obtain matching recommendation analysis data. The matching and recommendation module is used to obtain customer feedback data based on the matching and recommendation analysis data, and to adjust the recommendation analysis based on the customer feedback data until the matching and recommendation analysis data meets the customer feedback criteria.

[0071] The working principle and technical effects of the above solution are as follows: Comprehensive collection of structured and unstructured customer data, followed by cleaning and standardization to eliminate data clutter and inconsistent formats, addressing the limitations of traditional recommendation methods such as single data sources and low data quality; industry profiling analysis of the processed customer data to extract multi-dimensional industry characteristics, constructing hierarchical and dynamic industry profiles, accurately capturing the characteristics of the customer's industry, their own needs, and pain points, overcoming the limitations of traditional static profiles that cannot adapt to changing customer needs; multi-dimensional capability vector analysis of AI products, quantifying the capabilities of AI products into computable and matchable vector data, and performing bidirectional matching analysis with the constructed industry profiles, using computational capability differences to achieve ranking and recommendation of AI products, solving the problems of single matching logic and insufficient accuracy in traditional recommendation methods; collection of customer feedback, dynamically adjusting recommendation parameters and updating recommendation data based on feedback results, ensuring that the recommendation scheme can continuously adapt to changes in customer needs and guaranteeing the long-term stability of recommendation effects. The entire process is progressive and interconnected, with the output of each step serving as the input for the next, achieving full automation and intelligence, minimizing manual intervention, and improving recommendation efficiency and accuracy.

[0072] This method addresses the issues of non-standardized data processing and low data utilization in traditional recommendation methods. Through comprehensive data collection and standardized processing, it integrates structured and unstructured customer data, achieving unified management and efficient utilization of customer data and avoiding the problem of incomplete demand mining caused by single data types. It also solves the problem of traditional recommendation methods lacking accurate industry profiles and being unable to adapt to differentiated customer needs. By constructing dynamic industry profiles, it can accurately capture the characteristics of the customer's industry, the customer's own business features, and core needs, breaking the limitations of static profiles and enabling synchronous updates of profiles with customer needs and industry changes, making recommendation solutions more targeted. Furthermore, it addresses the problems of simplistic matching logic and low recommendation accuracy in traditional recommendation methods. Through multi-dimensional AI capability vector analysis and bidirectional matching, it accurately matches AI product capabilities with customer needs, avoiding blind and ineffective recommendations, improving the rationality and adaptability of recommendation solutions, and making recommended AI products more aligned with the actual business needs of customers. This solution addresses the problem of traditional recommendation methods lacking feedback optimization mechanisms and failing to continuously improve recommendation effectiveness. Through closed-loop feedback adjustment, it can collect customer feedback in a timely manner, dynamically optimize recommendation parameters and schemes, ensure that recommendation effectiveness can continuously adapt to changes in customer needs, and improve customer recognition and satisfaction with the recommendation scheme.

[0073] 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 enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching, characterized in that... The method includes: S1. Collect and process customer structured and unstructured data to obtain customer-collected and processed data; S2. Perform industry profile analysis based on customer-collected and processed data to obtain industry profile analysis data, and construct dynamic industry profiles based on the industry profile analysis data. S3. Perform capability vector analysis on AI products to obtain capability vector analysis data. Combine dynamic industry profiles with capability vector analysis data for matching analysis to obtain matching recommendation analysis data. S4. Obtain customer feedback data based on the matching recommendation analysis data, and adjust the recommendation analysis based on the customer feedback data until the matching recommendation analysis data meets the customer feedback criteria.

2. The enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching according to claim 1, characterized in that, S1 includes: The customer's structured data is collected using a preset structured data collection method to obtain the first set of collected data; The second set of data is obtained by collecting unstructured customer data through a preset unstructured data collection method. Based on the first set of collected data, a data acquisition framework is constructed to obtain data acquisition framework construction information; Perform frame filling analysis on the collected frame construction information to obtain frame filling analysis data; Based on the frame filling analysis data, fill feature extraction is performed on the second collected data to obtain frame filling extracted data. The data is extracted based on the framework and used to populate the data collection framework construction information to obtain the customer's collected and processed data.

3. The enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching according to claim 2, characterized in that, The step of performing frame filling analysis on the collected frame construction information to obtain frame filling analysis data includes: The framework feature information is determined based on the information collected from the framework construction information, and the framework blank data list is determined based on the framework feature information. Calculate the data correlation degree between the first and second collected data based on the blank data list in the framework, and obtain the data correlation degree of the list; Compare the correlation degree of the list data with the preset list data correlation threshold to obtain list correlation comparison information; The unstructured data type required to fill each blank field is determined based on the list association comparison information; Based on the list association comparison information, the frame filling priority is set, the blank fields are sorted by priority, and the unstructured data corresponding to the high-priority blank fields are filled first, generating frame filling analysis data.

4. The enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching according to claim 1, characterized in that, S2 includes: Based on a pre-set industry knowledge base, multi-dimensional industry features are extracted from customer-collected and processed data to obtain industry profile extraction data. Perform industry profile layer analysis on the extracted industry profile data to obtain industry profile layer analysis data; Based on the industry profile layer analysis data, we perform profile dimension weight analysis to obtain profile dimension weight data. Industry profiles are constructed by combining industry profile layering analysis data with profile dimension weight analysis data to obtain industry profile construction data. Verify and update the data used to build industry profiles to obtain dynamic industry profiles.

5. The enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching according to claim 4, characterized in that, The process of extracting industry profile data and performing industry profile layering analysis to obtain industry profile layering analysis data includes: The industry data is extracted by performing basic information extraction, feature information extraction, and demand information extraction at the basic layer, feature layer, and demand layer to obtain basic layer data, feature layer data, and demand layer data. Calculate the correlation between the base layer data and the feature layer data to obtain the first correlation data; Calculate the correlation between the feature layer data and the demand layer data to obtain the second correlation data; Calculate the correlation between the basic layer data and the demand layer data to obtain the third correlation data; The basic layer data, feature layer data, and demand layer data are combined based on the first correlation data, the second correlation data, and the third correlation data to obtain industry profile layered analysis data.

6. The enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching according to claim 4, characterized in that, The step of performing profile dimension weight analysis based on industry profile stratification analysis data to obtain profile dimension weight data includes: Weighted influencing factors are determined based on industry profile stratification analysis data; The weights of each dimension of the industry profile data are calculated by using the analytic hierarchy process to obtain the dimension weight calculation data. Based on the preset industry demand weight standards, the dimensional weight calculation data is adjusted to obtain weight adjustment data; The weight adjustment data is normalized to obtain the profile dimension weight data.

7. The enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching according to claim 4, characterized in that, The process of constructing industry profiles based on industry profile hierarchical analysis data and profile dimension weight analysis data, resulting in industry profile construction data, includes: A basic industry profile model is built based on industry profile hierarchical analysis data and profile dimension weight analysis data. Using the three-layer structure of industry profile hierarchical analysis data as a framework, import all analysis data from the basic layer, feature layer, and demand layer, and establish the correlation mapping relationship between the data of each layer. Based on the portrait dimension weight data, assign weights to each data dimension, increase the proportion of high-weight dimension data and decrease the proportion of low-weight dimension data. The data from each layer is merged to eliminate data redundancy and conflicts, resulting in merged profile data. The fused profile data is standardized and converted into a structured format that matches the capabilities of AI products to generate industry profile building data.

8. The enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching according to claim 1, characterized in that, S3 includes: Obtain preset capability analysis indicator information, analyze the AI ​​product's capability indicators based on the preset capability analysis indicator information, and obtain AI product capability indicator analysis data. Generate AI product capability indicator vector data based on AI product capability indicator analysis data, and obtain product capability vector analysis data. Based on the preset capability analysis indicators, the industry profile is analyzed to obtain the industry profile capability indicator analysis data. Based on the industry profile capability index analysis data, generate industry profile capability index vector data to obtain profile capability vector analysis data; The difference between the product capability vector analysis data and the profile capability vector analysis data of each AI product capability indicator analysis data is obtained to acquire capability gap information. The capability gap information of multiple AI products corresponding to the dynamic industry profile is sorted from largest to smallest to obtain matching recommendation analysis data.

9. The enterprise-level intelligent product recommendation method based on dynamic industry profiling and multi-dimensional AI capability matching according to claim 1, characterized in that, S4 includes: Personalized recommendation reports are pushed to customers based on matching recommendation analysis data; Based on the personalized recommendation report, feedback information is collected to obtain recommendation feedback collection data; The collected recommendation feedback data is compared with the preset recommendation feedback threshold to obtain recommendation comparison data; Based on the recommendation comparison data, the recommendation status is determined, and recommendation status determination data is obtained. The matching recommendation analysis data is updated based on the recommendation status determination data, and then adjusted until the matching recommendation analysis data meets the customer feedback standards.

10. A system for implementing the enterprise-level intelligent product recommendation method based on dynamic industry profiles and multi-dimensional AI capability matching as described in claim 1, characterized in that, The system includes: The data acquisition and processing module is used to collect and process customer structured and unstructured data to obtain customer data. The profile building module is used to perform industry profile analysis based on customer collected and processed data, obtain industry profile analysis data, and build dynamic industry profiles based on the industry profile analysis data. The capability analysis module is used to perform capability vector analysis on AI products, obtain capability vector analysis data, and combine dynamic industry profiles with capability vector analysis data for matching analysis to obtain matching recommendation analysis data. The matching and recommendation module is used to obtain customer feedback data based on the matching and recommendation analysis data, and to adjust the recommendation analysis based on the customer feedback data until the matching and recommendation analysis data meets the customer feedback criteria.

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