Contract generation method and device, computer equipment and computer storage medium

Abstract

The invention discloses a contract generation method and device, computer equipment and a computer storage medium, and relates to the technical field of contract generation. The method comprises the following steps: constructing a multi-source knowledge fusion engine according to multi-dimensional contract basic data, wherein the multi-dimensional contract basic data comprises legal dimension text information, industry dimension text information, historical contract case information and enterprise dimension text information; obtaining contract signing party information and contract prompt words, wherein the contract signing party information comprises enterprise credit analysis data and enterprise abnormal data; inputting the signing party information and the contract prompt word into a multi-source knowledge fusion engine, and performing semantic reasoning on the signing party information and the contract prompt word through the multi-source knowledge fusion engine to obtain an initial contract file; and revising the initial contract file based on an AI technology to obtain a target contract file. Therefore, through term semantic reasoning, it is ensured that the generated contract not only meets the legal requirements, but also reflects the industry characteristics and the enterprise risk control strategy, and meanwhile, the contract generation and review efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of contract generation, and particularly relates to a contract generation method and device, computer equipment and computer storage medium. BACKGROUND

[0002] The existing contract generation method depends on the completeness of the preset association library, needs to pre-construct a clause library, a guide library and a risk association relationship, if the association rule does not cover a new business scenario, it may lead to risk omission or matching error, at the same time, the user needs to answer the guide question layer by layer, the process is cumbersome and the subjective selection bias may affect the accuracy of the contract, leading to low contract generation efficiency. SUMMARY

[0003] Therefore, the present application aims to overcome the deficiencies in the prior art, and provides a contract generation method, device, computer equipment and computer storage medium, which can quickly generate a contract, improve compliance audit by several times, and effectively shorten the signing period.

[0004] The present application provides the following technical solutions: In a first aspect, the present application provides a contract generation method, comprising: constructing a multi-source knowledge fusion engine according to multi-dimensional contract basic data, wherein the multi-dimensional contract basic data comprises legal dimension text information, industry dimension text information, historical contract case information and enterprise dimension text information; obtaining signing party information and contract prompt words, wherein the signing party information comprises enterprise credit analysis data and enterprise abnormal data; inputting the signing party information and the contract prompt words into the multi-source knowledge fusion engine, performing semantic reasoning on the signing party information and the contract prompt words through the multi-source knowledge fusion engine, and obtaining an initial contract file; based on AI technology, revising the initial contract file to obtain a target contract file.

[0005] In an embodiment, the multi-source knowledge fusion engine is constructed according to multi-dimensional contract basic data, comprising: annotating preset legal key information in the legal dimension text information to obtain first annotation information; annotating preset industry key information in the industry dimension text information to obtain second annotation information; annotating historical contract case information meeting a preset contract completion degree condition to obtain third annotation information; annotating preset enterprise key information in the enterprise dimension text information to obtain fourth annotation information; The NLP model is trained according to the first annotation information, the second annotation information, the third annotation information and the fourth annotation information, so as to obtain the multi-source knowledge fusion engine.

[0006] In an embodiment, the multi-source knowledge fusion engine comprises an information extraction module, a risk assessment engine and a clause generator, and semantic reasoning is performed on the contracting party information and the contract prompt words by the multi-source knowledge fusion engine to obtain an initial contract file, which comprises: The information extraction module is used to match key information according to the contracting party information and the contract prompt words; The risk assessment engine is used to determine contract abnormal information according to the contracting party information and the contract prompt words; The clause generator is used to generate the initial contract file according to the key information and the contract abnormal information.

[0007] In an embodiment, the initial contract file is revised based on AI technology to obtain a target contract file, which comprises: The AI technology is used to perform standardized content identification and revision on the initial contract file to obtain a preprocessed contract file; The preprocessed contract file is subjected to clause risk rating to obtain a clause risk rating result; The preprocessed contract file is revised according to the clause risk rating result to obtain the target contract file.

[0008] In an embodiment, after the initial contract file is revised based on AI technology to obtain a target contract file, the method comprises: Key clauses in the target contract file are obtained, and historical adjudication documents corresponding to the key clauses are obtained based on a semantic similarity algorithm; Clause support degrees corresponding to the key clauses are obtained according to the historical adjudication documents; The target contract file is subjected to key clause annotation according to the clause support degrees to obtain an annotated contract file.

[0009] In an embodiment, after the initial contract file is revised based on AI technology to obtain a target contract file, the method comprises: When a legal regulation database is updated, updated clauses are obtained; If the target contract file contains the updated clauses, review prompt information of the target contract file is generated, and the updated clauses in the target contract file are identified.

[0010] In a second aspect, the present application provides a contract generation device, which comprises: The construction module is configured to construct a multi-source knowledge fusion engine according to multi-dimensional contract basic data, wherein the multi-dimensional contract basic data comprises legal dimension text information, industry dimension text information, historical contract case information and enterprise dimension text information; The acquisition module is configured to acquire contractee information and contract prompt words, wherein the contractee information comprises enterprise credit analysis data and enterprise abnormal data. The generation module is configured to input the contractee information and the contract prompt words into the multi-source knowledge fusion engine, perform semantic reasoning on the contractee information and the contract prompt words through the multi-source knowledge fusion engine, and obtain an initial contract file. The revision module is configured to revise the initial contract file based on an AI technology, and obtain a target contract file.

[0011] In an embodiment, the construction module is further configured to mark preset legal key information in the legal dimension text information to obtain first marked information, mark preset industry key information in the industry dimension text information to obtain second marked information, mark historical contract case information meeting preset contract completion degree conditions to obtain third marked information, mark preset enterprise key information in the enterprise dimension text information to obtain fourth marked information, and train an NLP model according to the first marked information, the second marked information, the third marked information and the fourth marked information to obtain the multi-source knowledge fusion engine.

[0012] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the contract generation method according to the first aspect.

[0013] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the contract generation method according to the first aspect.

[0014] The contract generation method, device, computer equipment and computer storage medium disclosed by the application construct a multi-source knowledge fusion engine according to multi-dimensional contract basic data, wherein the multi-dimensional contract basic data includes legal dimension text information, industry dimension text information, historical contract case information and enterprise dimension text information; contract prompt words and signing party information are obtained, wherein the signing party information includes enterprise credit analysis data and enterprise abnormal data; the signing party information and the contract prompt words are input into the multi-source knowledge fusion engine, semantic reasoning is performed on the signing party information and the contract prompt words through the multi-source knowledge fusion engine, and an initial contract file is obtained; the initial contract file is revised based on AI technology, and a target contract file is obtained. In this way, through clause semantic reasoning, it is ensured that the generated contract not only meets the legal requirements, but also embodies the industry characteristics and enterprise risk control strategies, and the contract generation and review efficiency is improved. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope of protection of the present application. In each drawing, similar components are denoted by similar reference numerals.

[0016] Figure 1 A flowchart of the contract generation method proposed in the embodiment is shown; Figure 2 Another flowchart of the contract generation method proposed in the embodiment is shown; Figure 3 Still another flowchart of the contract generation method proposed in the embodiment is shown; Figure 4 A device diagram of the contract generation device proposed in the embodiment is shown.

[0017] Explanation of the drawing: 400 - contract generation device; 401 - construction module; 402 - acquisition module; 403 - generation module; 404 - revision module. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments.

[0019] The components of the embodiments of the present application described and illustrated herein can be arranged and designed in a wide variety of different configurations. Therefore, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, but is merely representative of selected embodiments of the application. All other embodiments not explicitly described or shown herein, which would still be within the scope of the present application, are intended to be protected.

[0020] Hereinafter, the terms "include", "have", and their conjugates, used in the various embodiments of the present application, merely indicate the presence of the features, numbers, steps, operations, elements, components, or combinations thereof, and do not preclude the possibilities of the presence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations thereof.

[0021] In addition, the terms "first", "second", "third", and the like are used only to distinguish descriptions, and should not be understood as indicating or implying relative importance.

[0022] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present application belong. The terms (such as terms defined in generally used dictionaries) should be interpreted as having the same meaning as the contextual meaning in the relevant technical fields and should not be interpreted as having idealized or overly formal meanings, unless clearly defined in various embodiments of the present application.

[0023] Embodiment 1 The embodiments of the present disclosure provide a contract generation method for quickly generating a contract, improving compliance audit by several times, and effectively shortening the signing period.

[0024] Please refer to Figure 1 The contract generation method includes steps S101-S104, which will be described in detail below.

[0025] Step S101, constructing a multi-source knowledge fusion engine according to multi-dimensional contract basic data, the multi-dimensional contract basic data including legal dimension text information, industry dimension text information, historical contract case information and enterprise dimension text information.

[0026] In the embodiment, a multi-source knowledge fusion engine is constructed according to multi-dimensional contract basic data. The multi-source knowledge fusion engine is a trained natural language processing model and can be used to generate a contract. The multi-dimensional contract basic data includes legal dimension text information, industry dimension text information, historical contract case information and enterprise dimension text information. The legal dimension text information includes legal provision information applicable to each industry and each enterprise. The industry dimension text information includes industry-specific information and general information of each industry (mechanical, electronic, software, etc.). The historical contract case information includes documents formed in the judicial or arbitration procedure of contract dispute cases that have occurred in the past of each industry and each enterprise. The enterprise dimension text information includes rule and standard information of each enterprise in the industry to which the enterprise belongs, etc. The accuracy, comprehensiveness, compliance and commercial practicability of contract generation are greatly improved, so that the contract generation is no longer a single-dimensional text analysis tool, but a generation engine deeply integrating law, industry, judicial practice and enterprise internal rules.

[0027] In a specific embodiment, step S101 includes: labeling preset legal key information in the legal dimension text information to obtain first labeled information; labeling preset industry key information in the industry dimension text information to obtain second labeled information; labeling historical contract case information meeting a preset contract completion degree condition to obtain third labeled information; labeling preset enterprise key information in the enterprise dimension text information to obtain fourth labeled information; and training an NLP model according to the first labeled information, the second labeled information, the third labeled information and the fourth labeled information to obtain the multi-source knowledge fusion engine.

[0028] In the embodiment, preset legal key information in the legal dimension text information is labeled to obtain first labeled information. The preset legal key information is illegal and irregular information, which can be used to train the multi-source knowledge fusion engine to avoid contract violation of legal regulations.

[0029] The preset industry key information in the industry dimension text information is labeled to obtain second labeled information. The preset industry key information is industry rule information, which enables the model to have industry professional knowledge and ensures that the contract meets the industry rules.

[0030] The historical contract case information meeting the preset contract completion degree condition is labeled to obtain third labeled information. The preset contract completion degree condition can be a relatively high legal completion degree (for example, 80%). The legal completion degree is the completeness and rigor of a contract text in the legal aspect, thereby ensuring that the contract information with a high legal completion degree is learned from a large amount of historical contract case information.

[0031] The preset enterprise key information in the enterprise dimension text information is marked to obtain fourth marked information, wherein the preset enterprise key information is enterprise rule information, so as to ensure that the contract conforms to the enterprise rule.

[0032] Further, the natural language processing (NLP) model of the Transformer architecture is trained according to the first marked information, the second marked information, the third marked information and the fourth marked information, so as to obtain a multi-source knowledge fusion engine for generating a contract.

[0033] In step S102, contractee information and contract prompt words are obtained, wherein the contractee information includes enterprise credit analysis data and enterprise abnormal data.

[0034] In this embodiment, the third-party enterprise dimension text information query interface is called to obtain the contractee information and the contract prompt words, wherein the contractee information includes enterprise credit analysis data and enterprise abnormal data, and specifically, the enterprise abnormal data is judicial risk data of the enterprise.

[0035] In step S103, the contractee information and the contract prompt words are input into the multi-source knowledge fusion engine, and semantic reasoning is performed on the contractee information and the contract prompt words by the multi-source knowledge fusion engine to obtain an initial contract file.

[0036] In this embodiment, the contractee information and the contract prompt words are input into the multi-source knowledge fusion engine, and semantic reasoning is performed on the contractee information and the contract prompt words by the multi-source knowledge fusion engine to obtain a corresponding initial contract file, so that a contract meeting the situation and needs of the contractee can be quickly generated by using the natural language processing model.

[0037] Please refer to Figure 2 In a specific embodiment, the multi-source knowledge fusion engine includes an information extraction module, a risk assessment engine and a clause generator, and step S103 includes steps S1031-S1033, which will be described in detail below.

[0038] In step S1031, the information extraction module is used to match key information according to the contractee information and the contract prompt words.

[0039] In this embodiment, the information extraction module is used to analyze the contractee information and the contract prompt words, and match and extract the key information most related to the situation and needs of the contractee.

[0040] In step S1032, the risk assessment engine is used to determine contract abnormal information according to the contractee information and the contract prompt words.

[0041] In this embodiment, the risk assessment engine is used to analyze the contractee information and the contract prompt words, and predict the contract abnormal information.

[0042] In step S1033, the initial contract file is generated by the clause generator according to the key information and the contract exception information.

[0043] In this embodiment, the complete contract draft, i.e., the initial contract file, is generated by the clause generator by parsing the key information and the contract exception information.

[0044] In step S104, the initial contract file is revised based on an AI technology to obtain a target contract file.

[0045] In this embodiment, the initial contract file is revised based on the AI technology to obtain a more accurate target contract file.

[0046] See Figure 3 In a specific embodiment, step S104 includes steps S1041-S1043, which are described in detail as follows.

[0047] In step S1041, the initial contract file is standardized content-identified and revised based on the AI technology to obtain a preprocessed contract file.

[0048] In this embodiment, the AI technology is used to standardize the content identification and revision of the format clauses and the cited regulations in the initial contract file, so as to reduce the formal errors and citation errors in the obtained preprocessed contract file.

[0049] In step S1042, the preprocessed contract file is clause risk rated to obtain a clause risk rating result.

[0050] In this embodiment, the suspected controversial clauses in the preprocessed contract file are clause risk rated to obtain a clause risk rating result. The suspected controversial clauses include limitation of liability, intellectual property ownership, etc.

[0051] In step S1043, the preprocessed contract file is revised according to the clause risk rating result to obtain the target contract file.

[0052] In this embodiment, the preprocessed contract file is revised according to the clause risk rating result to obtain the target contract file.

[0053] In a specific embodiment, after step S104, the following steps are included: key clauses in the target contract file are obtained, and historical adjudication documents corresponding to the key clauses are obtained based on a semantic similarity algorithm; clause support degrees corresponding to the key clauses are obtained according to the historical adjudication documents; the target contract file is key clause annotated according to the clause support degrees to obtain an annotated contract file.

[0054] In the embodiment, the key clauses in the target contract file are acquired, wherein the key clauses are further determined as controversial clauses in the target contract file.

[0055] Further, based on the semantic similarity algorithm, the key clauses are matched with a plurality of historical adjudication documents in which the key clauses are acquired from the AI knowledge base.

[0056] Further, the clause support degree corresponding to the key clauses is acquired according to the adoption results of the historical adjudication documents, wherein the clause support degree = adoption times / (adoption times + rejection times) x 100%.

[0057] Further, the key clauses in the target contract file are annotated with the corresponding clause support degrees, and the annotated contract file is obtained, so that the key attention parts of the contract can be understood according to the annotated contract file.

[0058] In a specific embodiment, after step S104, the method further includes: when the legal regulation database is updated, acquiring an updated clause; if the target contract file contains the updated clause, generating a re-examination prompt information of the target contract file, and identifying the updated clause in the target contract file.

[0059] In the embodiment, when it is detected that the legal regulation database is updated, the updated clause is acquired; if the target contract file contains the updated clause, the re-examination prompt information of the target contract file is generated, and the updated clause in the target contract file is identified, so that it can be reminded in time whether the contract needs to be updated.

[0060] The contract generation method provided in the embodiment constructs a multi-source knowledge fusion engine according to multi-dimensional contract basic data, wherein the multi-dimensional contract basic data includes legal dimension text information, industry dimension text information, historical contract case information and enterprise dimension text information; acquires contracting party information and contract prompt words, wherein the contracting party information includes enterprise credit analysis data and enterprise abnormal data; inputs the contracting party information and the contract prompt words into the multi-source knowledge fusion engine, performs semantic reasoning on the contracting party information and the contract prompt words through the multi-source knowledge fusion engine, and obtains an initial contract file; revises the initial contract file based on AI technology, and obtains a target contract file. In this way, through clause semantic reasoning, it is ensured that the generated contract not only meets the legal requirements, but also embodies the industry characteristics and enterprise risk control strategies, and the contract generation and review efficiency is improved.

[0061] Embodiment 2 In addition, the disclosure embodiment provides a contract generation device 400, please see Figure 4 The device comprises: The construction module 401 is configured to construct a multi-source knowledge fusion engine according to multi-dimensional contract basic data, wherein the multi-dimensional contract basic data comprises legal dimension text information, industry dimension text information, historical contract case information and enterprise dimension text information; The acquisition module 402 is configured to acquire contractee information and contract prompt words, wherein the contractee information comprises enterprise credit analysis data and enterprise abnormal data. The generation module 403 is configured to input the contractee information and the contract prompt words into the multi-source knowledge fusion engine, perform semantic reasoning on the contractee information and the contract prompt words through the multi-source knowledge fusion engine, and obtain an initial contract file. The revision module 404 is configured to revise the initial contract file based on an AI technology, and obtain a target contract file.

[0062] In an embodiment, the construction module 401 is further configured to mark preset legal key information in the legal dimension text information to obtain first marked information, mark preset industry key information in the industry dimension text information to obtain second marked information, mark historical contract case information meeting a preset contract completion degree condition to obtain third marked information, mark preset enterprise key information in the enterprise dimension text information to obtain fourth marked information, and train an NLP model according to the first marked information, the second marked information, the third marked information and the fourth marked information to obtain the multi-source knowledge fusion engine.

[0063] In an embodiment, the multi-source knowledge fusion engine comprises an information extraction module, a risk assessment engine and a clause generator, and the generation module 403 is further configured to match key information according to the contractee information and the contract prompt words through the information extraction module, determine contract abnormal information according to the contractee information and the contract prompt words through the risk assessment engine, and generate the initial contract file according to the key information and the contract abnormal information through the clause generator.

[0064] In an embodiment, the revision module 404 is further configured to perform standardized content identification and revision on the initial contract file based on the AI technology to obtain a pretreated contract file, perform clause risk rating on the pretreated contract file to obtain a clause risk rating result, and revise the pretreated contract file according to the clause risk rating result to obtain the target contract file.

[0065] In an embodiment, the revising module 404 is further configured to acquire key clauses in the target contract file, and acquire historical judicial documents corresponding to the key clauses based on a semantic similarity algorithm; acquire clause support degrees corresponding to the key clauses according to the historical judicial documents; and perform key clause labeling on the target contract file according to the clause support degrees, to obtain a labeled contract file.

[0066] In an embodiment, the revising module 404 is further configured to acquire updated clauses when a legal regulation database is updated; if the target contract file contains the updated clauses, generate a re-examination prompt information of the target contract file, and identify the updated clauses in the target contract file.

[0067] The apparatus provided by the embodiments of the present disclosure can perform the steps of the contract generation method provided in Embodiment 1, and thus details are not repeated.

[0068] The contract generation apparatus provided by the embodiments of the present disclosure constructs a multi-source knowledge fusion engine according to multi-dimensional contract basic data, acquires signing party information and contract prompt words, inputs the signing party information and the contract prompt words into the multi-source knowledge fusion engine, performs semantic reasoning on the signing party information and the contract prompt words through the multi-source knowledge fusion engine, revises the initial contract file based on AI technology, and obtains a target contract file. In this way, through clause semantic reasoning, it is ensured that the generated contract meets legal requirements, embodies industry characteristics and enterprise risk control strategies, and improves contract generation and review efficiency.

[0069] Embodiment 3 In addition, the embodiments of the present disclosure provide a computer device including a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to implement the contract generation method of Embodiment 1.

[0070] The device provided by the embodiments of the present disclosure can perform the steps of the contract generation method provided in Embodiment 1, and thus details are not repeated.

[0071] Embodiment 4 The embodiments of the present disclosure provide a computer readable storage medium storing a computer program, and the computer program is executed by a processor to implement the contract generation method of Embodiment 1.

[0072] In the embodiment, the computer readable storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, etc.

[0073] The computer readable storage medium provided in the embodiment can implement the contract generation method provided in the embodiment 1, and thus, the description is not repeated here.

[0074] In all the examples shown and described herein, any specific values should be interpreted as merely exemplary and not limiting, and thus, other examples of the example embodiments can have different values.

[0075] It should be noted that like reference numerals and letters refer to like items throughout the drawings, and thus, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings.

[0076] The above described embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the present application. It should be pointed out that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A contract generation method characterized by, The method comprises the following steps: constructing a multi-source knowledge fusion engine according to multi-dimensional contract basic data, wherein the multi-dimensional contract basic data comprises legal dimension text information, industry dimension text information, historical contract case information and enterprise dimension text information; obtaining contract parties information and contract prompt words, wherein the contract parties information comprises enterprise credit analysis data and enterprise abnormal data; inputting the contract parties information and the contract prompt words into the multi-source knowledge fusion engine, and performing semantic reasoning on the contract parties information and the contract prompt words through the multi-source knowledge fusion engine to obtain an initial contract file; revising the initial contract file based on AI technology to obtain a target contract file.

2. The contract generation method of claim 1, wherein, The step of constructing the multi-source knowledge fusion engine according to multi-dimensional contract basic data comprises the following steps: annotating preset legal key information in the legal dimension text information to obtain first annotation information; annotating preset industry key information in the industry dimension text information to obtain second annotation information; annotating historical contract case information meeting preset contract completion degree conditions to obtain third annotation information; annotating preset enterprise key information in the enterprise dimension text information to obtain fourth annotation information; training an NLP model according to the first annotation information, the second annotation information, the third annotation information and the fourth annotation information to obtain the multi-source knowledge fusion engine.

3. The contract generation method of claim 1, wherein, The multi-source knowledge fusion engine comprises an information extraction module, a risk assessment engine and a clause generator, and the step of obtaining the initial contract file by performing semantic reasoning on the contract parties information and the contract prompt words through the multi-source knowledge fusion engine comprises the following steps: matching key information according to the contract parties information and the contract prompt words through the information extraction module; determining contract abnormal information according to the contract parties information and the contract prompt words through the risk assessment engine; generating the initial contract file according to the key information and the contract abnormal information through the clause generator.

4. The contract generation method of claim 1, wherein, The step of revising the initial contract file based on AI technology to obtain the target contract file comprises the following steps: performing standardized content identification and revision on the initial contract file based on the AI technology to obtain a preprocessed contract file; performing clause risk rating on the preprocessed contract file to obtain a clause risk rating result; revising the preprocessed contract file according to the clause risk rating result to obtain the target contract file.

5. The contract generation method of any one of claims 1 to 4, wherein, After the step of revising the initial contract file based on AI technology to obtain the target contract file, the method further comprises the following steps: obtaining key clauses in the target contract file, and obtaining historical judicial documents corresponding to the key clauses based on a semantic similarity algorithm; obtaining clause support degrees corresponding to the key clauses according to the historical judicial documents; performing key clause annotation on the target contract file according to the clause support degrees to obtain an annotated contract file.

6. The contract generation method of any one of claims 1 to 4, wherein, After the step of revising the initial contract file based on AI technology to obtain the target contract file, the method further comprises the following steps: when a legal regulation database is updated, obtaining updated clauses; If the target contract file exists the update clause, a review prompt information of the target contract file is generated, and the update clause in the target contract file is identified.

7. A contract generating apparatus characterized by comprising: The method comprises the steps of: The construction module is configured to construct a multi-source knowledge fusion engine according to multi-dimensional contract basic data, wherein the multi-dimensional contract basic data comprises legal dimension text information, industry dimension text information, historical contract case information and enterprise dimension text information. The acquisition module is configured to acquire contracting party information and contract prompt words, wherein the contracting party information comprises enterprise credit analysis data and enterprise abnormal data. The generation module is configured to input the contracting party information and the contract prompt words into the multi-source knowledge fusion engine, perform semantic reasoning on the contracting party information and the contract prompt words through the multi-source knowledge fusion engine, and obtain an initial contract file. The revision module is configured to revise the initial contract file based on an AI technology, and obtain a target contract file.

8. The contract generating apparatus according to claim 7, wherein The construction module is further configured to mark preset legal key information in the legal dimension text information to obtain first marking information, mark preset industry key information in the industry dimension text information to obtain second marking information, mark historical contract case information meeting a preset contract completion degree condition to obtain third marking information, and mark preset enterprise key information in the enterprise dimension text information to obtain fourth marking information. The NLP model is trained according to the first marking information, the second marking information, the third marking information and the fourth marking information, and the multi-source knowledge fusion engine is obtained. The computer program is stored in the memory and is executed by the processor to implement the contract generation method according to any one of claims 1 to 6. The computer program is stored in the memory and is executed by the processor to implement the contract generation method according to any one of claims 1 to 6.

9. A computer device, comprising: ​ 10. A computer-readable storage medium, characterized in that, ​

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