Contract generation method and system based on artificial intelligence

Abstract

The invention provides a contract generation method and system based on artificial intelligence. Belongs to the technical field of artificial intelligence and comprises the steps that a user inputs basic information and personalized requirements of a contract through an interactive interface, and a management system analyzes the input information, extracts key features and constructs a requirement vector; constructing an intelligent decision model based on a reinforcement learning algorithm; a database containing various contract templates is constructed, and the management system selects the most matched template from the template database as a starting point of contract generation according to the output of the reinforcement learning model. Through an artificial intelligence technology, especially natural language processing and a reinforcement learning algorithm, user demands can be rapidly analyzed and contract texts meeting the requirements can be generated, so that the time of traditional manual contract writing is greatly shortened.

Description

Technical Field

[0001] The present invention proposes a contract generation method and system based on artificial intelligence, belonging to the field of artificial intelligence technology. Background Art

[0002] Traditional contract generation relies on manual writing or filling in fixed templates. This approach is inadequate for handling complex and ever-changing contract requirements. Although contract generation methods based on natural language processing have emerged in recent years, these methods often lack sufficient flexibility and personalization to meet the specific needs of different users. Therefore, a more intelligent, flexible, and personalized contract generation method is needed. Summary of the Invention

[0003] The present invention provides a contract generation method and system based on artificial intelligence to solve the problems mentioned in the above background technology: The present invention proposes an artificial intelligence-based contract generation method, which includes: S1. The user enters basic contract information and personalized requirements through an interactive interface. The management system parses the input information, extracts key features, and constructs a demand vector. S2. Build an intelligent decision-making model based on reinforcement learning algorithm; S3. Build a database containing multiple contract templates. The management system selects the most matching template from the template database as the starting point for contract generation based on the output of the reinforcement learning model. S4. Based on the selected template, the management system uses natural language generation technology to fill in the specific content of the contract; S5. The generated contract text is automatically reviewed by the review module built into the management system. After the review is passed, the user downloads or prints the contract text and signs it.

[0004] The present invention proposes an artificial intelligence-based contract generation system, which includes a memory, a processor, and a computer program stored in and runnable on the memory. The processor executes the program to implement, for example, any of the artificial intelligence-based contract generation methods described above.

[0005] The beneficial effects of the present invention are as follows: through artificial intelligence technology, especially natural language processing and reinforcement learning algorithms, user needs can be quickly analyzed and a contract text that meets the requirements can be generated, greatly shortening the time of traditional manual contract writing; the use of deep learning models to perform semantic analysis and structured representation of contract terms can ensure the accuracy and compliance of the contract content. At the same time, by comparing and analyzing similar clauses and cases in the knowledge graph, users can enhance their understanding of the meaning, applicable scenarios, and legal consequences of contract clauses, thereby improving contract quality. Based on users' historical behavior and preferences, the system provides personalized contract generation suggestions and services to meet the specific needs of different users and enhance the user experience. The built-in audit module automatically checks for illegal clauses or missing necessary clauses in the contract text, and uses machine learning algorithms to deeply analyze potential risks, providing users with more secure and reliable contract texts. By analyzing user feedback and transaction results, the system continuously optimizes the construction and application process of feature vectors and knowledge graphs, and continuously iterates and upgrades the management system to adapt to changing market demands and legal requirements. By intelligently recommending clause combinations and additional services that meet user needs and issuing early warnings of potential legal risks and compliance issues, users can avoid legal disputes caused by improper contracts. Users only need to enter basic information and personalized requirements through an interactive interface, and the system automatically completes contract generation, review, and optimization, without the need for professional legal knowledge, which lowers the barrier to use. By collecting user feedback, the system continuously improves the contract generation service and improves user satisfaction and loyalty. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 This is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0007] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0008] One embodiment of the present invention, for example Figure 1 As shown, a contract generation method based on artificial intelligence, the method comprising: S1. The user enters basic contract information and personalized requirements through an interactive interface. The management system parses the input information, extracts key features, and constructs a demand vector. S2. Build an intelligent decision-making model based on reinforcement learning algorithm; S3. Build a database containing multiple contract templates. The management system selects the most matching template from the template database as the starting point for contract generation based on the output of the reinforcement learning model. S4. Based on the selected template, the management system uses natural language generation technology to fill in the specific content of the contract; S5. The generated contract text is automatically reviewed by the review module built into the management system. After the review is passed, the user downloads or prints the contract text and signs it.

[0009] The above technical solution works as follows: Users enter basic contract information and personalized requirements through an interactive interface such as a graphical user interface (GUI) or command line interface (CLI). This information includes, but is not limited to, the contract type (e.g., lease, purchase), information about the two or more parties (e.g., name, address, contact information), and specific transaction terms (e.g., price, delivery date, liability for breach of contract). After receiving the user's input, the management system first parses it and extracts key features. These features include key contract terms and specific requirements of the transaction parties. Based on the extracted key features, the management system constructs a requirements vector. This vector is a mathematical representation that serves as input to the subsequent intelligent decision-making model, enabling it to understand and generate contracts that meet the user's requirements. A reinforcement learning algorithm is then used to construct the intelligent decision-making model. Reinforcement learning is a machine learning paradigm in which an intelligent agent learns how to take optimal actions to maximize cumulative rewards by interacting with its environment. The intelligent decision-making model learns by simulating a large number of contract generation scenarios. In these simulations, the model generates contract structure and content based on the demand vectors and receives rewards or penalties based on the simulation results (e.g., user satisfaction, contract validity, etc.). Through trial and error and a reward mechanism, the model gradually optimizes its decision-making strategy and learns how to generate the optimal contract based on the demand vectors. A database of various contract templates is constructed. These templates cover common contract types and structures, providing a starting point for contract generation. Based on the output of the reinforcement learning model, the management system selects the most suitable template from the template database. Adaptive algorithms are used to personalize the selected template to meet the user's specific needs. This involves modifying clauses, adding or removing specific content, etc. Based on the selected template, the management system uses natural language generation technology to fill in the contract content. This involves generating coherent and accurate sentences and paragraphs to express the various clauses and details in the contract. The generated content undergoes grammatical and semantic checking to ensure that there are no grammatical errors, unclear semantics, or logical contradictions. The system also provides intelligent optimization capabilities to fine-tune the contract text based on user feedback and actual needs. This may involve adjusting wording, adding explanatory content, or simplifying complex clauses. The management system's built-in review module automatically reviews the generated contract text. This includes checking the legality, compliance and completeness of the contract; for example, if the contract text passes the review, the management system will allow the user to download or print the contract text; the user can sign the generated contract text, thus completing the contract generation and signing process.

[0010] The above technical solution has the following effects: Through automated processes, the time from user requirement input to contract generation is significantly shortened, improving work efficiency. Users only need to enter basic information and personalized requirements through an interactive interface, eliminating the need to manually write or modify the contract text, simplifying the operation process. Intelligent decision-making models and adaptive algorithms can personalize contract templates based on user input demand vectors and feedback, ensuring that the generated contract text meets the user's specific requirements. Users can use the system's intelligent optimization function to fine-tune the contract text, further enhancing the contract's flexibility and applicability. Natural language generation technology and grammatical and semantic checking ensure the accuracy and fluency of the contract text, reducing contract disputes caused by inappropriate language expression. Automated review of the contract text through a built-in audit module can promptly identify and correct potential compliance issues, ensuring that the contract complies with relevant laws and regulations. The application of automated processes and intelligent decision-making models significantly reduces the workload of manually writing, reviewing, and modifying contract texts, reducing labor costs. The personalized, convenient, and efficient contract generation process can improve user satisfaction and loyalty. By providing accurate, compliant, and user-specific contract texts, users' trust and reliance on the system are enhanced.

[0011] In one embodiment of the present invention, the S1 includes: S11. The interface dynamically adjusts the input box based on the contract type selected by the user, displaying relevant required and optional items; identity verification is performed before key information is entered; S12. The user enters a contract overview or specific requirements in the text box. The management system uses natural language processing technology to analyze the user's intention and convert it into structured data. S13. Users can select or exclude specific terms through the pre-set terms library and choose the applicable jurisdiction based on the transaction location or the agreement between the two parties; S14. Construct a multi-dimensional feature vector based on the information input by the user; utilize the contract domain knowledge graph to enhance the understanding of the contract structure and terms; S15. Record the user's historical behavior and learn the user's preferences based on machine learning algorithms.

[0012] The above technical solution works as follows: When a user selects a contract type (e.g., commercial lease, sale of goods) through an interactive interface, the interface dynamically adjusts the input fields based on the selected contract type, displaying the required and optional fields relevant to that contract type. This dynamic adjustment makes the interface more intuitive and user-friendly while ensuring that users can enter all necessary information. Before entering key information, the system performs identity verification to ensure that the user has legal authority to generate the contract. This typically involves username and password verification, or higher-level authentication methods such as two-factor authentication. Users enter a contract overview or specific requirements in a text field. The management system uses natural language processing (NLP) to parse this text to understand the user's intent and requirements. The parsed user intent and requirements are converted into structured data to facilitate subsequent processing and decision-making. This includes extracting key information, identifying the contract type and required terms, and selecting or excluding specific terms from a pre-set library of terms, such as liability for breach of contract and confidentiality agreements. The system automatically adjusts the terms to comply with local legal requirements based on the transaction location or the parties' agreement. Users can request additional services such as legal consulting, contract translation, and electronic signature services. The system records these requests and arranges subsequent processes, such as contacting service providers and providing quotes. Based on the information entered by the user, it constructs a multidimensional feature vector. These features include contract type, transaction size, legal jurisdiction, and required terms. These feature vectors are important inputs for subsequent intelligent decision-making models. The system leverages contract domain knowledge graphs to enhance understanding of contract structure and terms. The knowledge graph, which contains specialized knowledge, legal terms, and common cases in the contract field, helps improve the accuracy of feature extraction and the performance of intelligent decision-making models. The system records historical user behavior, including previously generated contract types, selected terms, and requested additional services. It also learns user preferences based on machine learning algorithms. By analyzing historical user behavior, the system can identify user preferences and habits, thereby providing more personalized services and recommendations during subsequent contract generation.

[0013] The effects of the above technical solution are: dynamically adjusting the input box according to the contract type selected by the user, making the interface more concise and clear, and the user only needs to pay attention to the required and optional items related to the selected contract type, which reduces the difficulty of operation and improves the user experience; identity verification is performed before entering key information to ensure the accuracy and security of user information and enhance the user's trust in the system; natural language processing technology is used to parse user intentions and convert them into structured data, so that the system can understand user needs more accurately and generate contract texts that better meet user expectations; users select or exclude specific terms through a preset terms library, and the system automatically adjusts the terms to comply with local laws based on the transaction location or the agreement between the two parties, which greatly improves the efficiency and accuracy of contract generation and reduces the risk of disputes caused by inappropriate legal terms; users can request additional services, such as legal Consulting services, contract translation services, electronic signature services, etc. The system records and arranges subsequent processes, which provides users with a more convenient and comprehensive service experience and increases the added value of the contract; constructs a multi-dimensional feature vector based on the information input by the user, including contract type, transaction size, legal jurisdiction, and clause requirements, which provides rich input information for the intelligent decision-making model and helps to generate more accurate and personalized contract texts; uses the contract domain knowledge graph to enhance the understanding of contract structure and clauses, improves the accuracy of feature extraction, and further enhances the intelligence level of contract generation; records the user's historical behavior, which helps the system understand the user's preferences and habits; learns the user's preferences based on the machine learning algorithm, so that the system can predict the user's future needs based on the user's past behavior, thereby providing more personalized contract generation suggestions and services.

[0014] In one embodiment of the present invention, the S14 includes: S141. Based on the information input by the user, extract basic features such as contract type (e.g., commercial lease, sale of goods), transaction size (e.g., amount, quantity), legal jurisdiction, and clause requirements, incorporate contextual features into a multidimensional feature vector, and dynamically update the multidimensional feature vector. S142. Integrate information from multiple sources, including legal databases, industry norms, and historical contract cases, to construct a knowledge graph for the contract field. The knowledge graph represents contract-related entities, attributes, and relationships in the form of a graph structure. Utilize entity recognition and relationship extraction techniques within the knowledge graph to perform semantic parsing and structured representation of contract terms. S143. Enhance understanding of the meaning, applicable scenarios, and legal consequences of contract terms by comparing and analyzing similar terms and cases in the knowledge graph; S144. Based on the analysis results of multi-dimensional feature vectors and knowledge graphs, intelligently recommend combinations of terms and additional services that meet user needs; at the same time, provide early warnings of potential legal risks and compliance issues; S145. Based on the feature fusion algorithm, the multi-dimensional feature vector is deeply integrated with the information in the knowledge graph, and deep learning technology is applied to perform pattern recognition and classification on the integrated feature vector; S146. By training deep learning models, we can automatically identify different types of contract models and potential risk points. Based on the results of fusion analysis, we can provide users with personalized contract writing suggestions, clause optimization solutions, and additional service recommendations. S147. At the same time, based on user feedback and transaction results, the construction and application process of feature vectors and knowledge graphs are continuously optimized, and the management system is continuously iterated and upgraded.

[0015] The above technical solution works as follows: Based on user input, it first extracts basic features such as contract type (e.g., commercial lease, sale of goods), transaction size (e.g., amount, quantity), legal jurisdiction, and clause requirements. In addition to these basic features, contextual features are incorporated into a multidimensional feature vector. These contextual features include the credit ratings of both parties, historical transaction records, and market changes. These contextual features help to more comprehensively understand the transaction context and user needs. As user input increases and the transaction progresses, the multidimensional feature vector is dynamically updated. For example, when a user selects or excludes a specific clause, the clause requirement feature is updated promptly; when the transaction size changes, the transaction size feature is adjusted. This dynamic update ensures the timeliness and accuracy of the feature vector. It also integrates information from multiple sources, such as legal databases, industry standards, and historical contract cases, to construct a contract-related knowledge graph. This knowledge graph represents contract-related entities, attributes, and relationships in a graph-like structure, providing a foundation for subsequent semantic parsing and structured representation. Entity recognition and relationship extraction techniques within the knowledge graph are used to perform semantic parsing and structured representation of contract terms. This helps the system more accurately understand the meaning and applicable scenarios of contract terms. By comparing and analyzing similar terms and cases in the knowledge graph, the system enhances understanding of the meaning, applicable scenarios, and legal consequences of contract terms. It also helps the system provide users with more accurate and personalized contract drafting advice. Based on the analysis of multi-dimensional feature vectors and the knowledge graph, it intelligently recommends clause combinations and additional services that meet user needs. This recommendation not only considers the user's basic needs and contextual characteristics, but also incorporates professional knowledge and historical experience in the contract field. Furthermore, the system issues early warnings of potential legal risks and compliance issues, alerting users to take appropriate preventative measures. Based on a feature fusion algorithm, the multi-dimensional feature vectors are deeply integrated with information in the knowledge graph. This integration not only improves the richness and accuracy of the feature vectors but also provides more comprehensive input for subsequent deep learning models. Deep learning techniques are applied to perform pattern recognition and classification on the fused feature vectors. By training the deep learning model, it automatically identifies different types of contract models and potential risk points. Based on the results of the fusion analysis, the system provides users with personalized contract drafting advice, clause optimization solutions, and additional service recommendations. These suggestions and optimization solutions not only consider the specific needs and contextual characteristics of users, but also incorporate professional knowledge and historical experience in the contract field. Based on user feedback and transaction results, the construction and application of feature vectors and knowledge graphs are continuously optimized. This continuous optimization ensures the adaptability and accuracy of the system. The management system is continuously iterated and upgraded, introducing new technologies and algorithms to enhance the system's intelligence and user experience.

[0016] The effects of the above technical solutions are as follows: by extracting and analyzing the multi-dimensional feature vectors input by users and combining them with the rich information in the knowledge graph, the system can intelligently recommend clause combinations and additional services that meet user needs, greatly improving the personalization and intelligence level of services; the system can automatically identify potential legal risks and compliance issues, and issue timely warnings to help users avoid risks and ensure transaction security; as the user input information continues to increase and the transaction process advances, the multi-dimensional feature vectors can be dynamically updated to ensure the timeliness and accuracy of the information; using the entity recognition and relationship extraction technology in the knowledge graph, the contract terms are semantically parsed and structured, which improves the efficiency and accuracy of contract processing; through feature fusion algorithms and deep learning technology, the system can automatically identify different types of contract models and potential risk points, and provide users with personalized contract writing suggestions and clause optimization solutions; based on user feedback and transaction results, the system can continuously optimize the construction and application process of feature vectors and knowledge graphs, achieve continuous iteration and upgrading, and ensure the advancement and applicability of the system; integrate multi-source information to build a knowledge graph in the contract field, and provide comprehensive knowledge support for contract management and decision-making; based on the analysis results of multi-dimensional feature vectors and knowledge graphs, the system can provide users with intelligent decision-making support to help users make more informed choices; the system interface is user-friendly and easy to operate, and users can easily complete the input of contract information and the selection of clauses; the system provides comprehensive services such as contract generation, risk warning, writing suggestions and optimization solutions to meet the diverse needs of users and enhance user experience and satisfaction.

[0017] In one embodiment of the present invention, the step S145 includes: Preprocess the extracted multi-dimensional feature vectors and extract entities, attributes, and relationships that are highly relevant to the current contract context from the knowledge graph to form a local knowledge subgraph. Utilize graph embedding technology to convert knowledge subgraphs into low-dimensional vector representations, enhancing information expressiveness and computational efficiency; Through the fusion method based on the attention mechanism, the model dynamically adjusts its attention to the multi-dimensional feature vector and the knowledge graph vector; Build a deep learning framework that integrates pattern recognition and classification tasks of feature vectors; and use ensemble learning methods to combine the prediction results of multiple deep learning models Based on the deep learning model, the fine-grained contract pattern recognition algorithm can distinguish more specific contract subtypes or clause patterns; Apply clustering algorithms to conduct unsupervised learning on contract samples to discover potential contract categories or risk groups; Combining domain knowledge with the predictive power of deep learning models, it automatically identifies potential risk points in contracts, uses the compliance check module to conduct compliance assessments on contract terms, and provides immediate feedback. Based on the user feedback mechanism, user feedback is collected; based on user feedback and transaction results, the feature vectors, knowledge graph construction and parameters of the deep learning model are continuously adjusted and optimized to form a closed-loop iterative system.

[0018] The above technical solution works by cleaning the multidimensional feature vectors extracted from user input to remove redundant, duplicate, or invalid data. This includes checking data consistency and completeness, and addressing format inconsistencies. For missing values ​​in the feature vectors, appropriate filling strategies, such as mean filling, median filling, mode filling, or model-based prediction filling, are employed to ensure data integrity. Outliers in the feature vectors are identified and processed, and feature values ​​of different dimensions are converted to a consistent scale to improve model convergence speed and performance. From the knowledge graph, highly relevant entities, attributes, and relationships are extracted based on the current contract context to form a local knowledge subgraph. Graph embedding technologies such as Node2Vec and DeepWalk are used to convert the local knowledge subgraph into a low-dimensional vector representation. These vectors capture the underlying connections and similarities between entities, enhancing information expressiveness and computational efficiency. Using an attention mechanism, the model dynamically adjusts its focus on the multidimensional feature vectors and knowledge graph vectors. A deep learning framework, such as a convolutional neural network (CNN), recurrent neural network (RNN), or graph neural network (GNN), is constructed to integrate feature vectors for pattern recognition and classification tasks. These models can extract high-level features from complex data and automatically learn classification boundaries. They employ ensemble learning methods, such as random forests and gradient boosting machines, to combine the predictions of multiple deep learning models. Building on deep learning models, fine-grained contract pattern recognition algorithms are developed. These algorithms can distinguish more specific contract subtypes or clause patterns, providing users with more refined contract drafting recommendations. Clustering algorithms (such as K-means and DBSCAN) are applied to contract samples for unsupervised learning. By combining domain knowledge with the predictive power of deep learning models, potential risks in contracts, such as legal loopholes and unfair clauses, are automatically identified. Furthermore, a compliance check module assesses the compliance of contract clauses against industry norms, laws, and regulations, providing immediate feedback. A user feedback mechanism is established to collect feedback on contract recommendations and risk warnings. Based on user feedback and transaction results, feature vectors, knowledge graph construction, and deep learning model parameters are continuously adjusted and optimized. This forms a closed-loop iterative system, enabling the model to continuously adapt to market changes and evolving user needs.

[0019] The effects of the above technical solutions are as follows: through data cleaning, missing value filling and outlier processing, the quality and integrity of the data are effectively improved, providing a reliable foundation for subsequent feature fusion and deep learning model training; making the feature values ​​of different dimensions in the same scale range helps the deep learning model to better capture the correlation and difference between features, thereby improving the prediction performance of the model; extracting entities, attributes and relationships that are highly relevant to the current contract context from the knowledge graph, forming a concise and informative local knowledge subgraph, providing rich background knowledge for subsequent deep learning models; converting the local knowledge subgraph into a low-dimensional vector representation not only enhances the expressiveness of information, but also improves computational efficiency, enabling deep learning models to process complex knowledge graph data more efficiently; through the attention mechanism, the model can dynamically adjust its attention to multi-dimensional feature vectors and knowledge graph vectors, thereby more accurately capturing key information in the contract context and improving the prediction accuracy of the model; the constructed deep learning framework can fuse the model of feature vectors It can distinguish more specific contract subtypes or clause patterns, providing users with more refined contract writing suggestions, which helps reduce contract risks and improve contract efficiency. Through unsupervised learning, it discovers potential contract categories or risk groups, providing strong support for risk warning and compliance checks. Combining domain knowledge with the predictive ability of deep learning models, it automatically identifies potential risk points in contracts, such as legal loopholes and unfair clauses, and uses the compliance check module to conduct compliance assessments on contract clauses, effectively reducing contract risks. By collecting user feedback on contract recommendations, risk warnings, etc., it continuously understands user needs and preferences, providing valuable guidance for the continuous optimization of the model. Based on user feedback and transaction results, it continuously adjusts and optimizes feature vectors, knowledge graph construction, and deep learning model parameters, forming a closed-loop iterative system that enables the model to continuously adapt to market changes and the evolution of user needs.

[0020] In one embodiment of the present invention, the S2 includes: S21. Decompose the contract generation process into multiple subtasks, each corresponding to a reinforcement learning model; and design a reward function based on the corresponding factors of contract generation to guide the model to learn the optimal strategy; S22. Introducing adversarial examples to simulate potential contract review challenges, using a large amount of historical contract data to simulate different contract generation scenarios and train reinforcement learning models; S23. Iteratively optimize the model strategy through continuous trial and error and reward feedback until it converges to the optimal solution; and regularly evaluate the model performance.

[0021] The above technical solution works by breaking down the contract generation process into multiple specific subtasks, such as clause selection, clause order optimization, and clause detail filling. These subtasks are independent yet interrelated, forming the complete contract generation process. For each subtask, a reward function is designed based on factors related to contract generation. This reward function aims to guide the model to learn the optimal strategy to ensure that the generated contract meets both regulatory requirements (compliance) and user expectations (user satisfaction), while also improving contract generation efficiency (e.g., reducing generation time). To simulate potential contract review challenges, adversarial examples are introduced. These examples may include common review issues or controversial points, testing the model's performance in complex scenarios. A large amount of historical contract data is used to simulate various contract generation scenarios. These scenarios may involve different types of contracts, transaction conditions, or legal environments. The adversarial examples and data from the simulated scenarios are fed into a reinforcement learning model for training. The model continuously learns and adjusts its strategy to adapt to various complex situations. During training, the model gradually optimizes its strategy through trial and error and receiving reward feedback. This process is iterative until the model converges to an optimal solution, generating high-quality, efficient contracts that meet user needs. The model's performance is regularly evaluated, including metrics such as the contract approval rate (i.e., whether the contract has passed review) and user satisfaction. These evaluation results guide model tuning to ensure continuous improvement and adaptability to the evolving market and legal landscape.

[0022] The above technical solution achieves the following: By breaking the contract generation process into multiple subtasks and equipping each with a dedicated reinforcement learning model, each subtask can more efficiently handle each step of contract generation. Each model focuses on its own task, such as clause selection, sequence optimization, and detail filling, thereby improving the accuracy and efficiency of the overall generation process. The designed reward function guides the model to learn the optimal strategy, ensuring that the generated contracts strike an optimal balance between quality (compliance, user satisfaction) and efficiency (generation time). This helps reduce unnecessary duplication of work and manual intervention, improving the overall efficiency of contract generation. Introducing adversarial examples to simulate potential contract review challenges helps enhance the model's robustness in the face of complex and uncertain situations. This training method enables the model to learn more generalizable features, making it better adaptable to various scenarios in real-world contract generation. Utilizing a large amount of historical contract data to simulate different contract generation scenarios helps the model demonstrate greater adaptability to various contract types and conditions. This ensures that the model can generate high-quality contracts under diverse legal environments, transaction conditions, and user needs. Through iterative optimization through trial and error and reward feedback, the model gradually converges to the optimal solution. This continuous optimization mechanism ensures that the model's performance continues to improve over time and with the accumulation of data. Regular evaluation of model performance, including indicators such as the pass rate of contract generation and user satisfaction, helps to promptly identify model issues and optimize them. This ensures that the model can continue to meet user needs and market demands. The generated contracts are of high quality and strong compliance, which can reduce disputes and risks caused by contract issues and enhance user trust and satisfaction. High contract generation efficiency can shorten transaction cycles and improve overall business efficiency, thereby providing users with a more convenient and efficient service experience.

[0023] In one embodiment of the present invention, the step S21 includes: Treat clause selection in the contract generation process as an independent subtask, and intelligently select clause combinations from the preset clause library based on user needs and contract type; Through a policy gradient-based reinforcement learning model, the optimal clause selection strategy is learned through trial and error, and the order of different clause combinations is learned to optimize the logical structure and reading experience of the contract; Generate detailed text that conforms to grammatical and semantic rules based on contextual information through a natural language generation model based on deep learning; Based on the preset compliance reward function, the compliance of the contract is evaluated by comparing the generated contract text with the preset compliance rule library, and corresponding rewards or penalties are given; By collecting user feedback data on the generated contract, such as ratings and comments, the user's satisfaction with the contract is evaluated and corresponding rewards are given based on the satisfaction.

[0024] The working principle of the above technical solution is as follows: a preset clause library containing various common contract clauses is established. These clauses cover different types of contracts and transaction scenarios, providing a rich resource for subsequent clause selection. Based on the user's specific needs and contract type, the system intelligently selects the most appropriate clause combination from the clause library. This process may involve parsing user input, identifying the contract type, and using an algorithm to match clauses with requirements. Using a policy gradient-based reinforcement learning model, the system continuously learns the optimal clause selection strategy through trial and error. During this process, the model continuously adjusts its clause selection strategy based on the current contract generation state and the preset reward mechanism to maximize long-term rewards. In addition to selecting clauses, the model also learns the order of different clause combinations. This helps optimize the contract's logical structure and reading experience, making the generated contract clearer and easier to understand. After determining the clause selection and order, the system uses a deep learning-based natural language generation model to generate detailed text that conforms to grammatical and semantic rules based on contextual information. This process ensures that the generated contract text is both accurate and fluent. The system also presets a compliance reward function to evaluate whether the generated contract text conforms to the preset compliance rule library. These rule bases may contain requirements such as laws, regulations, and industry standards. Based on the results of compliance assessments, the system issues corresponding rewards or penalties. This helps guide the model to generate more compliant contract texts and reduce potential legal risks. The system collects user feedback on generated contracts, such as ratings and comments. This data reflects users' evaluation of contract quality, compliance, and overall satisfaction. Based on user satisfaction ratings, the system issues corresponding rewards, helping to improve the quality of model generation and better meet user expectations and needs.

[0025] The above technical solution achieves the following: It treats clause selection as a separate subtask and, utilizing a pre-set clause library, intelligently selects the most appropriate clause combination based on user needs and contract type. This approach significantly reduces the workload of manual screening and matching, improving the efficiency and accuracy of clause selection. Through continuous trial and error learning through a reinforcement learning model, the system gradually understands the differences between user needs and contract types, thereby providing more personalized clause selection solutions to meet users' specific needs. The policy gradient-based reinforcement learning model not only learns clause selection strategies but also the ordering of different clause combinations. This helps optimize the logical structure of contracts, making them clearer and more organized. By optimizing the order of clauses and the generation of detailed text, the system generates contracts that are easier to understand and read, improving the user reading experience. Using a deep learning-based natural language generation model, the system generates detailed text that adheres to grammatical and semantic rules based on contextual information. This ensures that the generated contract text is both accurate and fluent, avoiding grammatical errors and unclear semantics. Through the natural language generation model, the system generates contract text that contains rich details, meeting users' requirements for detailed contract content. Based on a pre-set compliance reward function, the system performs compliance assessments on the generated contract text. It helps ensure that the generated contract complies with the requirements of laws, regulations and industry standards, and reduces potential legal risks; by giving corresponding rewards or punishments, the system can guide the model to generate more compliant contract texts and improve the compliance level of the contract; by collecting user feedback data on the generated contract, such as ratings and comments, the system can understand the user's evaluation of the quality, compliance and overall satisfaction of the contract text; based on the user's satisfaction evaluation results, the system can continuously adjust and optimize the model to generate contract texts that better meet user expectations and needs, thereby improving user satisfaction.

[0026] In one embodiment of the present invention, S3 includes: S31. Regularly obtain the latest industry contract templates, update the template library, and carefully classify and tag the templates; S32. Combining feature vectors and template tags, a multi-dimensional matching algorithm is used to quickly match the template that best matches the user's needs. Based on the matching results, a preliminary contract text is generated and a preview function is provided for the user to review and adjust. S33. Intelligently adjust clause content based on user demand vectors and template structure, utilize natural language generation technology to fill in contract details, and perform syntax and semantic checks; S34. Collect user feedback on the initially generated contract, and perform personalized adjustments and optimization on the initially generated contract based on the user feedback.

[0027] The above technical solution works as follows: the system regularly obtains the latest contract templates from authoritative industry channels or partners to ensure the timeliness and comprehensiveness of the template library. The obtained templates are carefully categorized by industry (e.g., real estate, finance, technology), transaction type (e.g., leasing, sales, services), and other dimensions, and each template is labeled accordingly. This facilitates the rapid identification and matching of templates with user requirements. Templates are then evaluated for quality, including completeness of clauses, clarity of wording, and legal compliance. This ensures that only high-quality templates are included in the template library, providing users with a reliable foundation for contract generation. Based on the user's input requirements (e.g., contract type, transaction parties, and terms), the system extracts feature vectors and performs a multi-dimensional match with template tags in the template library. An algorithm calculates similarity to quickly identify the template that best matches the user's requirements. Based on the matching results, the system generates a preliminary contract text. Users can preview the generated contract text and make adjustments or confirmations as needed. The system intelligently adjusts the terms based on the user's requirement vector and the template structure. This includes adding or deleting specific clauses and adjusting the order of clauses to meet the user's personalized needs. Using natural language generation technology, the system populates contract details such as dates, amounts, and information about both parties. At the same time, the system also performs grammatical and semantic checks on the generated contract text to ensure its accuracy and fluency. The system collects user feedback on the initially generated contract, such as satisfaction ratings and modification suggestions. This data provides an important basis for subsequent personalized adjustments and optimizations. Based on user feedback, the system makes personalized adjustments to the generated contract text. This includes modifying clause content and adjusting contract structure to better meet user expectations and needs. By continuously learning and accumulating user feedback data, the system continuously optimizes algorithms and models to improve the accuracy and efficiency of contract generation.

[0028] The effects of the above technical solutions are: through template library management and updating, users are ensured to have access to the latest and most comprehensive contract templates; the quick matching and preliminary generation functions enable users to quickly obtain contract texts that meet their needs, greatly shortening the contract generation time; template quality assessment ensures the completeness of the template's terms and clarity of expression, providing a solid foundation for contract generation; the intelligent adjustment and detail filling functions use natural language generation technology to ensure the accuracy and fluency of the contract and reduce human errors; through user feedback and personalized optimization functions, the system can continuously learn and adapt to user needs and generate contract texts that better meet user expectations; this flexibility enables the system to be widely used in different industries and different transaction types to meet the personalized needs of different users; the preview function enables users to intuitively view the contract content, facilitating adjustments and optimizations; the personalized adjustment and optimization functions enable users to feel the intelligence and adaptability of the system, enhancing user experience and satisfaction.

[0029] In one embodiment of the present invention, the step S32 includes: For each contract template, we use a deep learning algorithm to extract features from the template text and construct a feature vector containing multiple dimensions. Based on the existing tag system, semantic tags and emotional tags are introduced to further enrich and improve template tags. At the same time, the association relationship between tags is used to build a tag network. Using the constructed feature vector, the cosine similarity is used to calculate the similarity between the user demand vector and the template feature vector; based on the similarity sorting, the template set closest to the user demand is preliminarily screened out; Based on the preliminary matching, templates that meet multiple tag requirements are screened out according to the tag combination required by the user; at the same time, the association relationship in the tag network is used to further optimize the screening results; Dynamically adjust the weights of each dimension based on the user's historical choices and feedback data; finally, based on the weighted comprehensive score, select the optimal template as the basis for generating the preliminary contract text; Based on the matching results, the content of the optimal template is quickly filled into the preliminary contract text; at the same time, the placeholders or variable parts in the template are retained.

[0030] The working principle of the above technical solution is as follows: For each contract template, a deep learning algorithm (such as a convolutional neural network (CNN), a recurrent neural network (RNN), or their variants) is used to extract features from the template text. A feature vector containing multiple dimensions is constructed, including but not limited to industry characteristics, transaction type characteristics, clause complexity characteristics, and expression style characteristics. These feature vectors can comprehensively reflect the text content and style of the template. Based on the existing labeling system (such as industry and transaction type), semantic and sentiment labels are introduced to further enrich and improve the template labels. The associations between labels are used to construct a label network. The label network can reveal the inherent connections between different labels, providing strong support for subsequent refined matching. Using the constructed feature vectors, algorithms such as cosine similarity are used to calculate the similarity between the user requirement vector and the template feature vector. The templates that are closest to the user requirement are initially selected based on the similarity ranking. This step can quickly narrow the matching scope and improve matching efficiency. Based on the initial matching, the template labels are further combined for refined matching. Based on the combination of user requirement labels, templates that meet multiple label requirements are selected. The associations in the label network are used to further optimize the selection results. For example, if a template simultaneously meets multiple associated tags required by a user, its priority will be increased. Based on the user's historical choices and feedback data, the weights of various dimensions (such as feature vector similarity and tag matching) are dynamically adjusted. Based on the weighted overall score, the optimal template is selected as the basis for generating the preliminary contract text. This step ensures that the selected template not only meets user needs but also has a high degree of match and satisfaction. Based on the matching results, the content of the optimal template is quickly populated into the preliminary contract text. Placeholders or variable parts in the template are retained to facilitate subsequent customization based on user needs. For example, placeholders can be replaced with specific dates, amounts, or information about both parties.

[0031] The above technical solution achieves the following: It extracts features from contract templates using a deep learning algorithm, constructing a feature vector encompassing multiple dimensions, including industry characteristics, transaction type characteristics, clause complexity characteristics, and expression style characteristics. This multi-dimensional feature representation more comprehensively and accurately reflects the content and characteristics of contract templates, thereby improving matching accuracy. Furthermore, semantic and sentiment tags are introduced to further enrich and refine the template tags, building upon the existing tagging system. This helps to more meticulously describe the attributes and characteristics of contract templates, making the matching process more precise. A tag network is constructed using the correlations between tags, revealing the inherent connections between different tags and providing strong support for refined matching. Through optimization of the tag network, contract templates that better meet user needs can be selected. Using the constructed feature vectors and tagging system, algorithms such as cosine similarity are used to quickly calculate the similarity between the user's requirement vector and the template's feature vector. The similarity ranking is then used to preliminarily select the template set that best matches the user's needs. This step quickly narrows the matching scope and improves matching efficiency. Based on the matching results, the content of the optimal template is quickly incorporated into the preliminary contract text, while retaining placeholders or variable portions within the template for subsequent customization. This rapid filling method significantly shortens contract generation time and improves work efficiency. Based on the initial matching, it further refines the matching process by combining template tags, selecting templates that meet multiple tag requirements based on the user's desired tag combinations. This refined matching approach better meets users' personalized needs. The weights of various dimensions are dynamically adjusted based on the user's historical choices and feedback data, ensuring that matching results are more aligned with user expectations and preferences. This dynamic adjustment method continuously improves matching accuracy and personalization. Placeholders or variable sections in the template are retained for subsequent customization based on user needs. This flexibility and customizability allows users to further modify and improve the contract text according to their needs, enhancing the user experience. By collecting historical user choices and feedback data, the matching algorithm and template library are continuously optimized and improved, improving matching accuracy and efficiency. This efficient feedback mechanism enables the system to continuously learn and adapt to changing user needs, enhancing the user experience.

[0032] In one embodiment of the present invention, the S4 includes: S41. Generate clauses that meet specific circumstances based on contract type and user needs, and standardize the generated contract text using the legal language model; S42. Use grammar checking tools to check for grammatical errors in the contract text, and use semantic understanding technology to check for semantic ambiguity in the contract text; S43. Compare different versions of the contract text and select the version with the best user feedback as the final output; provide personalized contract generation suggestions based on user historical behavior and preferences; S44. Based on the feedback loop mechanism, collect user feedback on the contract generation service and improve the service based on user feedback.

[0033] The above technical solution works as follows: Based on the contract type and user needs, natural language generation technologies (such as template filling and text generation models) are used to generate clauses tailored to the specific context. For example, in a commercial lease contract, a rent adjustment clause can be generated. Legal language models are used to normalize the generated contract text to ensure that it complies with legal regulations and industry practices. This includes adjusting the wording, format, and presentation to meet legal requirements. Logical analysis and association rule mining are used to check the interdependencies between clauses, ensuring the logical and consistent nature of the contract content. This helps avoid inconsistencies and conflicts between clauses. Grammar checking tools are used to check for grammatical errors, including spelling errors and punctuation errors. Semantic understanding technologies (such as semantic analysis and semantic role annotation) are used to check for semantic ambiguity in the contract text. This ensures that the clauses are clearly and accurately expressed, avoiding ambiguity and misunderstanding. Different versions of the contract text are compared, and the optimal version is selected as the final output based on user feedback. This helps ensure the quality of the contract text and meets user needs. Based on user historical behavior and preferences, machine learning algorithms or recommendation systems are used to provide personalized contract generation suggestions. This allows users to provide contract texts that better suit their needs and preferences. A feedback loop mechanism is established to collect user feedback on the contract generation service. This can include user evaluations and suggestions on aspects such as contract text quality, speed, and ease of use. Based on user feedback, the contract generation service is continuously improved. This can include optimizing algorithms, updating template libraries, and enhancing the user interface to continuously enhance service quality and user experience.

[0034] The above technical solution achieves the following: It generates clauses tailored to specific scenarios based on contract type and user needs, ensuring the professionalism and relevance of the contract text. For example, the rent adjustment clause in a commercial lease contract accurately reflects the rights and obligations of both parties to the lease. The generated contract text is standardized using a legal language model to ensure compliance with legal regulations and industry practices. This avoids legal disputes arising from inappropriate language, enhancing the professionalism and legal effectiveness of the contract. By checking the interdependencies between clauses, the logicality and consistency of the contract content are ensured. This helps construct a complete and coherent contract text, enhancing its readability and enforceability. A grammar checker detects grammatical errors, such as spelling errors and punctuation errors, improving the accuracy and fluency of the contract text and enhancing its professional image. Semantic understanding technology checks for semantic ambiguity in the contract text, ensuring the clarity and accuracy of the clauses. This helps avoid misunderstandings and disputes caused by unclear language, improving the transparency and credibility of the contract. Comparing different versions of the contract text, the version with the best user feedback is selected as the final output. This ensures the quality of contract texts and their compliance with user needs, improving user satisfaction. Based on user history and preferences, personalized contract generation suggestions are provided, helping to meet individual user needs and enhance user experience and loyalty. A feedback loop mechanism is established to collect user feedback on the contract generation service, helping to promptly identify service issues and deficiencies, providing data support for service improvements. Based on user feedback, the contract generation service is continuously improved and optimized, helping to enhance service quality and efficiency and meet users' ever-changing needs and expectations.

[0035] In one embodiment of the present invention, the S5 includes: S51. Use a rules engine to automatically review contract texts to check for illegal clauses or omissions of necessary clauses. Combined with machine learning algorithms, conduct in-depth analysis of contract texts to identify potential legal risks. S52. Establish a contract execution tracking system to record key milestones and events during contract execution; regularly conduct user satisfaction surveys to collect user feedback and suggestions on contract generation services; S53. Continuously optimize contract generation services based on user feedback and satisfaction survey results.

[0036] The working principle of the above technical solution is as follows: the system has a built-in rule engine, which automatically scans and reviews the uploaded contract text according to the preset compliance requirements and contract terms standards; the rule engine can check whether the contract text contains any illegal terms, such as unfair terms, illegal terms, etc., and verify whether the contract omits necessary terms, such as information of both parties, subject matter of the contract, performance period, etc.; combined with machine learning algorithms, the system conducts in-depth semantic analysis of the contract text to identify potential legal risk points; by training a large amount of contract data, the machine learning model can accurately identify key terms and potential risks, providing valuable reference for auditors; the system generates a detailed audit result report, including the reasons for passing / failure of the audit, recommended modifications, etc.; the report is presented in an intuitive manner, so that users can quickly understand the compliance and potential risks of the contract text; the system provides contract text download functions in multiple formats (such as PDF, Word) to meet the different usage needs of users; users can easily print the contract text or sign it electronically; the system integrates The system provides an electronic signature service that supports users to complete contract signing online. The electronic signature service uses technical means such as digital certificates, hash algorithms, and encryption algorithms to ensure that the identity of the signing process is identifiable and the content cannot be tampered with, thereby improving signing efficiency and convenience. The system provides a signing status tracking function, and users can view the progress and status of contract signing at any time. This helps users to understand the contract signing status in a timely manner and ensure the smooth execution of the contract. A contract execution tracking system is established to record key nodes and events in the contract execution process. The system can monitor the execution of the contract in real time to ensure that the contract is performed as agreed. Regular user satisfaction surveys are conducted to collect user feedback and suggestions on the contract generation service. Satisfaction surveys help to understand user needs and service quality, and provide a basis for continuous optimization of services. The collected user feedback is sorted and analyzed to identify problems and deficiencies in the service. The contract generation service is continuously optimized based on user feedback and satisfaction survey results, including improving contract terms templates, optimizing review processes, and improving the convenience and security of electronic signature services.

[0037] The above technical solution achieves the following: through the combination of a rules engine and machine learning algorithms, it enables rapid, automated review of contract texts and in-depth legal risk assessment. This significantly shortens the review cycle, while improving the accuracy and comprehensiveness of the review and effectively reducing audit errors caused by human negligence or insufficient professional capabilities. The audit result report provides detailed information on the reasons for passing / failing the review and the recommended revisions, providing users with clear guidance and reference, helping them quickly understand and correct issues in the contract text. The contract text can be downloaded in multiple formats, such as PDF and Word, meeting the needs of users in different scenarios, making both printing and electronic signing more convenient. The integrated electronic signature service allows users to complete contract signing online, eliminating the need for paper document delivery or mailing, greatly improving signing efficiency and convenience. At the same time, electronic signatures have legal effect, ensuring the security and compliance of the signing process; providing a signing status tracking function, users can check the progress and status of contract signing at any time, which helps users to keep abreast of the contract signing situation and avoid delays or omissions; establishing a contract execution tracking system that can record key nodes and events in the contract execution process in real time, helping users to identify problems and take measures in a timely manner to ensure the smooth performance of the contract; regularly conducting user satisfaction surveys to collect user feedback and suggestions on contract generation services, helping to understand user needs and service quality, and providing a basis for service optimization; based on user feedback and satisfaction survey results, continuously optimizing contract generation services, including improving contract terms templates, optimizing review processes, and improving the convenience and security of electronic signature services, so as to continuously improve user experience and satisfaction.

[0038] One embodiment of the present invention is an artificial intelligence-based contract generation system, comprising a memory, a processor, and a computer program stored on and executable on the memory, wherein the processor executes the program to implement, for example, any of the artificial intelligence-based contract generation methods described above.

[0039] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A contract generation method based on artificial intelligence, characterized in that: The method comprises: S1. The user enters basic contract information and personalized requirements through an interactive interface. The management system parses the input information, extracts key features, and constructs a demand vector. S2. Build an intelligent decision-making model based on reinforcement learning algorithm; S3. Build a database containing multiple contract templates. The management system selects the most matching template from the template database as the starting point for contract generation based on the output of the reinforcement learning model. S4. Based on the selected template, the management system uses natural language generation technology to fill in the specific content of the contract; S5. The generated contract text is automatically reviewed by the review module built into the management system. After the review is passed, the user downloads or prints the contract text and signs it.

2. The artificial intelligence-based contract generation method according to claim 1, characterized in that: Said S1 comprises: S11. The interface dynamically adjusts the input box based on the contract type selected by the user, displaying relevant required and optional items; identity verification is performed before key information is entered; S12. The user enters a contract overview or specific requirements in the text box. The management system uses natural language processing technology to analyze the user's intention and convert it into structured data. S13. Users can select or exclude specific terms through the pre-set terms library and choose the applicable jurisdiction based on the transaction location or the agreement between the two parties; S14. Based on the information input by the user, a multi-dimensional feature vector is constructed, and the understanding of the contract structure and terms is enhanced by utilizing the contract domain knowledge graph; S15. Record the user's historical behavior and learn the user's preferences based on machine learning algorithms.

3. The artificial intelligence-based contract generation method according to claim 2, characterized in that: Said S14 comprises: Based on the information input by the user, basic features are extracted, contextual features are also incorporated into the multidimensional feature vector, and the multidimensional feature vector is dynamically updated; Integrate multi-source information to build a knowledge graph in the contract field; use entity recognition and relationship extraction technology in the knowledge graph to perform semantic analysis and structured representation of contract terms; By comparing and analyzing similar clauses and cases in the knowledge graph, we can enhance our understanding of the meaning, applicable scenarios, and legal consequences of contract clauses. Based on the analysis results of multi-dimensional feature vectors and knowledge graphs, it intelligently recommends clause combinations and additional services that meet user needs; at the same time, it provides early warnings of potential legal risks and compliance issues; Based on the feature fusion algorithm, the multi-dimensional feature vector is deeply integrated with the information in the knowledge graph, and deep learning technology is applied to perform pattern recognition and classification on the fused feature vector; By training deep learning models, it automatically identifies different types of contract models and potential risk points. Based on the results of fusion analysis, it provides users with personalized contract writing suggestions, clause optimization solutions, and additional service recommendations. At the same time, based on user feedback and transaction results, the construction and application process of feature vectors and knowledge graphs are continuously optimized, and the management system is continuously iterated and upgraded.

4. The artificial intelligence-based contract generation method according to claim 1, characterized in that: Said S2 comprises: S21. Decompose the contract generation process into multiple subtasks, each corresponding to a reinforcement learning model; and design a reward function based on the corresponding factors of contract generation to guide the model to learn the optimal strategy; S22. Introducing adversarial examples to simulate potential contract review challenges, using a large amount of historical contract data to simulate different contract generation scenarios and train reinforcement learning models; S23. Iteratively optimize the model strategy through continuous trial and error and reward feedback until it converges to the optimal solution; and regularly evaluate the model performance.

5. The artificial intelligence-based contract generation method according to claim 4, characterized in that: Said S21 comprises: Treat clause selection in the contract generation process as an independent subtask, and intelligently select clause combinations from the preset clause library based on user needs and contract type; Through a policy gradient-based reinforcement learning model, the optimal clause selection strategy is learned through trial and error, and the order of different clause combinations is learned to optimize the logical structure and reading experience of the contract; Generate detailed text that conforms to grammatical and semantic rules based on contextual information through a natural language generation model based on deep learning; Based on the preset compliance reward function, the compliance of the contract is evaluated by comparing the generated contract text with the preset compliance rule library, and corresponding rewards or penalties are given; By collecting user feedback data on the generated contract, such as ratings and comments, the user's satisfaction with the contract is evaluated and corresponding rewards are given based on the satisfaction.

6. The artificial intelligence-based contract generation method according to claim 1, characterized in that: Said S3 comprises: S31. Regularly obtain the latest industry contract templates, update the template library, and carefully classify and tag the templates; S32. Combine the feature vector and template tag, use a multi-dimensional matching algorithm to match a template that is close to the user's needs; generate a preliminary contract text based on the matching results, and provide a preview function for the user to review and adjust; S33. Intelligently adjust clause content based on user demand vectors and template structure, utilize natural language generation technology to fill in contract details, and perform syntax and semantic checks; S34. Collect user feedback on the initially generated contract, and perform personalized adjustments and optimization on the initially generated contract based on the user feedback.

7. The artificial intelligence-based contract generation method according to claim 1, characterized in that: The S32 includes: For each contract template, we use a deep learning algorithm to extract features from the template text and construct a feature vector containing multiple dimensions. Based on the existing tag system, semantic tags and emotional tags are introduced to further enrich and improve template tags. At the same time, the association relationship between tags is used to build a tag network. Using the constructed feature vector, the cosine similarity is used to calculate the similarity between the user demand vector and the template feature vector; based on the similarity sorting, the template set closest to the user demand is preliminarily screened out; Based on the preliminary matching, templates that meet multiple tag requirements are screened out according to the tag combination required by the user; at the same time, the association relationship in the tag network is used to further optimize the screening results; Dynamically adjust the weights of each dimension based on the user's historical choices and feedback data; finally, based on the weighted comprehensive score, select the optimal template as the basis for generating the preliminary contract text; Based on the matching results, the content of the optimal template is quickly filled into the preliminary contract text; at the same time, the placeholders or variable parts in the template are retained.

8. The artificial intelligence-based contract generation method according to claim 1, characterized in that: Said S4 comprises: S41. Generate clauses that meet specific circumstances based on contract type and user needs, and standardize the generated contract text using the legal language model; S42. Use grammar checking tools to check for grammatical errors in the contract text, and use semantic understanding technology to check for semantic ambiguity in the contract text; S43. Compare different versions of the contract text and select the version with the best user feedback as the final output; provide personalized contract generation suggestions based on user historical behavior and preferences; S44. Based on the feedback loop mechanism, collect user feedback on the contract generation service and improve the service based on user feedback.

9. The artificial intelligence-based contract generation method according to claim 1, characterized in that: Said S5 comprises: S51. Use a rules engine to automatically review contract texts to check for illegal clauses or omissions of essential clauses. Combined with machine learning algorithms, conduct in-depth analysis of contract texts to identify potential risks. S52. Establish a contract execution tracking system to record key milestones and events during contract execution; regularly conduct user satisfaction surveys to collect user feedback and suggestions on contract generation services; S53. Continuously optimize contract generation services based on user feedback and satisfaction survey results.

10. A contract generation system based on artificial intelligence, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the memory, wherein the processor executes the program to implement, for example, an artificial intelligence-based contract generation method as described in any one of claims 1 to 9.

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