Intelligent tax risk prediction system and implementation method thereof

Abstract

The invention relates to the technical field of tax risk prediction, and provides an intelligent tax risk prediction system, which comprises a data acquisition module, a feature engineering module, a risk assessment engine, an intelligent early warning module, an interpretability analysis module and a block chain evidence storage module. According to the intelligent tax risk prediction system and method, various advanced technologies and innovative algorithms are integrated, efficient, accurate, explainable and safe prediction of tax risks is achieved, the intelligent level and risk prevention and control capacity of tax management are greatly improved, and the system and method have wide application prospects and popularization value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tax risk prediction, in particular to an intelligent tax risk prediction system and an implementation method thereof. BACKGROUND

[0002] With the rapid development of economy and the continuous improvement of tax policies, the complexity and challenges faced by tax management are increasing. The tax-related activities of enterprises show diversification and dynamism, involving financial statements, invoice management, tax declaration, bank fund flow, and business registration data, with large data volume and complex structure. Traditional tax risk identification relies on manual experience and simple rules, which is difficult to meet the analysis needs of massive, multi-source and heterogeneous data, leading to late risk discovery, low accuracy, and difficulty in effectively identifying new and complex tax violations.

[0003] In recent years, with the development of big data, artificial intelligence and blockchain technologies, tax risk prediction has gradually shifted towards intelligentization. Deep learning models have shown strong capabilities in anomaly detection and behavior pattern recognition, knowledge graph technology can effectively reveal complex relationships between enterprises, and the fusion of multi-model methods has improved the robustness and accuracy of prediction. At the same time, the development of explainable artificial intelligence technology provides transparency support for tax risk models, enhancing the trust and understanding of regulatory authorities for prediction results.

[0004] However, existing intelligent tax risk prediction systems still have many shortcomings. On the one hand, data sources are scattered, lacking a unified real-time collection and secure storage mechanism, resulting in uneven data quality and affecting risk assessment effectiveness; on the other hand, single models are difficult to fully capture the multi-dimensional features of tax risk, making it difficult to accurately identify associated transaction risks and hidden abnormal behaviors. In addition, the lack of effective model explanation methods and data storage mechanisms reduces the usability and compliance of the system, making it difficult to meet the actual needs of tax regulation.

[0005] At the same time, data privacy protection and cross-institutional collaborative modeling issues are increasingly prominent. Enterprise tax-related data involves a large amount of sensitive information, and traditional centralized model training poses a risk of data leakage, limiting data sharing and collaboration among multiple institutions. Emerging technologies such as federated learning provide a solution to the contradiction between privacy protection and model optimization, but their application in the field of tax risk prediction is still in its infancy.

[0006] Therefore, the present application proposes an intelligent tax risk prediction system and an implementation method thereof to solve the above problems. SUMMARY

[0007] To overcome the defects of the prior art, the present application aims to provide an intelligent tax risk prediction system and an implementation method thereof.

[0008] To achieve the object, the technical scheme of the present application is implemented as follows: an intelligent tax risk prediction system comprises:

[0009] A data acquisition module is configured to acquire multi-dimensional tax-related data of an enterprise in real time, including financial data, invoice data, declaration data, bank transaction data and business registration information.

[0010] A feature engineering module is connected to the data acquisition module and configured to clean and standardize the acquired raw data and extract a tax risk feature vector.

[0011] A risk assessment engine comprises:

[0012] An abnormality detection unit based on deep learning is configured to identify abnormal transaction patterns using an auto-encoder network.

[0013] A knowledge graph construction unit is configured to construct a network of enterprise associations and identify associated transaction risks.

[0014] A multi-model fusion prediction unit is configured to integrate random forests, XGBoost and LSTM neural networks and output a risk score.

[0015] An intelligent early warning module is configured to generate graded early warning information based on the risk score and push the information to the corresponding management department.

[0016] An explainability analysis module is configured to use the SHAP algorithm to explain the prediction results and generate a risk factor contribution report.

[0017] A blockchain storage module is configured to store key prediction results and decision-making processes on a blockchain to ensure traceability and non-tamperability of the prediction process.

[0018] Preferably, the feature engineering module further comprises:

[0019] A time series feature extraction unit is configured to calculate time series features of the tax burden rate, the input-output ratio and the inventory turnover rate of the enterprise.

[0020] An industry benchmark comparison unit is configured to compare the enterprise indicators with the benchmark values of the same industry and generate deviation indicators.

[0021] A cross-feature generation unit is configured to generate high-order features by cross-combining features.

[0022] Preferably, the knowledge graph construction unit specifically comprises:

[0023] An entity recognition subunit is configured to identify entities such as enterprises, legal persons, shareholders, suppliers and customers.

[0024] A relationship extraction subunit is configured to extract relationships such as ownership, transactions and guarantees between entities.

[0025] A graph embedding subunit converts the graph structure into a vector representation using the Graph2Vec algorithm.

[0026] Preferably, an intelligent tax risk prediction method comprises the following steps:

[0027] S1: Collecting multi-source heterogeneous tax-related data of enterprises and establishing a unified data warehouse;

[0028] S2: Preprocessing the original data, including missing value filling, outlier processing and data standardization;

[0029] S3: Extracting multi-dimensional risk features, including:

[0030] Financial indicator features: calculating gross profit margin, net profit margin, asset-liability ratio, etc.

[0031] Behavioral pattern features: analyzing invoice frequency, declaration time regularity, and tax structure;

[0032] Correlation network features: constructing enterprise relationship graph and calculating network centrality and clustering coefficient;

[0033] S4: Building a hybrid prediction model:

[0034] Training supervised learning models using historical labeled data;

[0035] Using unsupervised learning to detect new risk patterns;

[0036] Optimizing risk threshold settings through reinforcement learning;

[0037] S5: Generating a risk assessment report, including risk level, risk type, key risk points and rectification suggestions.

[0038] Preferably, the hybrid prediction model in step S4 adopts the following innovative architecture:

[0039] First layer: using a Transformer encoder to process time series data and capture long-term dependencies;

[0040] Second layer: using a graph neural network to process enterprise relationship networks and learn structured features;

[0041] Third layer: integrating multi-source features through an attention mechanism and adaptively adjusting feature weights;

[0042] Output layer: generating a risk score of 0-100 and a risk category probability distribution.

[0043] Preferably, it also includes:

[0044] S6: Establishing a feedback learning mechanism, collecting tax inspection results, and updating model parameters;

[0045] S7: Using a federated learning framework, cross-institutional model optimization is realized under the premise of protecting enterprise privacy.

[0046] Preferably, the risk types include:

[0047] False invoicing risk, tax evasion risk, related party transaction risk, abnormal tax burden risk and false declaration risk.

[0048] Preferably, a computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method of any one of claims 4-7.

[0049] Preferably, an electronic device includes a processor and a memory, the memory stores a computer program, and the processor executes the computer program to implement the method of any one of claims 4-7.

[0050] The beneficial effects of the present application are embodied in:

[0051] The intelligent tax risk prediction system and method provided by the present application make full use of multi-source heterogeneous tax-related data, combine advanced data preprocessing and feature engineering technology, and significantly improve the expression ability and accuracy of tax risk features. Through the integration of deep learning anomaly detection, autoencoder network, knowledge graph construction and multi-model fusion prediction, the system can comprehensively identify various tax risks, including false invoicing, tax evasion, related party transaction risk, etc., and improve the sensitivity and accuracy of risk discovery.

[0052] Specifically, the data acquisition module of the present system realizes real-time acquisition and secure storage of financial, invoice, declaration, bank transaction and business information, ensuring the integrity and timeliness of the data. The feature engineering module fills in missing values, processes outliers, normalizes and extracts time series features, and generates cross-features in combination with industry benchmarks, effectively enhancing the expression of model input and improving the accuracy of risk assessment.

[0053] The risk assessment engine uses variational autoencoder for anomaly detection, builds an enterprise correlation network based on knowledge graph, and extracts structured features through graph embedding technology, enhancing the ability to identify complex related party transaction risks. The multi-model fusion strategy integrates random forest, XGBoost and LSTM neural network, realizes multi-angle and multi-dimensional risk scoring, reduces the possible bias of a single model, and improves the overall prediction performance.

[0054] The intelligent early warning module realizes differentiated early warning based on hierarchical risk scoring, and improves the response speed of risk information and the disposal efficiency of management departments by combining a multi-channel push mechanism. The explainable analysis module uses the SHAP algorithm to provide transparent risk factor contribution reports, enhances the trustworthiness of the system and the understanding of the prediction results by users, and helps to develop targeted rectification measures.

[0055] In addition, the blockchain evidence module realizes the non-tamperability and traceability of risk prediction results and decision-making processes through alliance chain technology, guarantees the security and compliance of tax data and prediction processes, and improves the application value of the system in supervision and auditing.

[0056] The method of the application also innovatively uses a Transformer encoder to capture long-term dependencies of time series data, combines a graph neural network to process enterprise relationship networks, and adaptively fuses multi-source features through an attention mechanism, which significantly improves the expression ability of the model and the accuracy of risk identification. The introduction of the feedback learning mechanism and the federated learning framework not only realizes the continuous optimization and dynamic adjustment of the model, but also promotes collaborative modeling across agencies while protecting enterprise privacy, enhancing the adaptability and potential for application of the system.

[0057] In summary, the intelligent tax risk prediction system and method of the application integrates various advanced technologies and innovative algorithms, realizes efficient, accurate, interpretable and secure prediction of tax risks, greatly improves the intelligent level and risk prevention and control capability of tax management, and has wide application prospects and promotional value. BRIEF DESCRIPTION OF DRAWINGS

[0058] In the drawings:

[0059] Fig. 1 is a system structure schematic diagram of the application;

[0060] Fig. 2 is a prediction method step schematic diagram of the application. DETAILED DESCRIPTION

[0061] The application will be further described in detail below in conjunction with the drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments. The embodiments in the application and the features in the embodiments can be combined with each other without conflict. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the application.

[0062] In addition, "multiple" refers to two or more. In addition, the technical solutions among various embodiments can be combined with each other, but it must be based on the fact that a person skilled in the art can realize it, and when the combination of technical solutions appears contradictory or cannot be realized, it should be considered that the combination of technical solutions does not exist, nor is it within the protection scope of the invention.

[0063] Please refer to the drawings in the description Figs. 1-2 :

[0064] Embodiment 1: Intelligent tax risk prediction system

[0065] The embodiment provides an intelligent tax risk prediction system, which comprises a data acquisition module, a feature engineering module, a risk assessment engine, an intelligent early warning module, an explainability analysis module and a blockchain storage module.

[0066] 1. Data acquisition module

[0067] The data acquisition module is responsible for real-time acquisition of multi-dimensional tax-related data of enterprises. In specific implementation, the module acquires data through the following interfaces:

[0068] Financial data interface: connected to the enterprise ERP system, collecting balance sheet, profit and loss statement, cash flow statement and other financial statement data, with a collection frequency of once a day;

[0069] Invoice data interface: connected to the tax bureau gold tax system, real-time acquisition of value-added tax invoice information of enterprises, including invoice code, number, amount, tax amount, and purchase and sale information;

[0070] Declaration data interface: acquisition of tax return table data of enterprises of various taxes, including value-added tax, enterprise income tax, stamp duty, etc.

[0071] Banking interface: through bank-enterprise direct connection or API interface, acquisition of income and expenditure details of enterprise bank accounts;

[0072] Business interface: connected to the business department database, acquisition of enterprise registration information, shareholder information, business scope, etc.

[0073] All collected data are transmitted in encrypted form and stored in a distributed database to ensure data security.

[0074] 2. Feature engineering module

[0075] The feature engineering module comprises a data preprocessing unit, a time series feature extraction unit, an industry benchmark comparison unit and a cross-feature generation unit.

[0076] The data preprocessing unit performs the following operations:

[0077] Missing Value Treatment: Fill in missing financial data using KNN interpolation method;

[0078] Outlier Detection: Identify and handle outliers using boxplot method;

[0079] Data Standardization: Use Z-score standardization method to unify data dimension.

[0080] Time Series Feature Extraction Unit calculates the following key indicators:

[0081] Tax Burden Rate Time Series: Calculate the actual tax burden rate trend in the past 12 months;

[0082] Input-Output Ratio: Analyze the proportion of input tax and output tax and its fluctuations;

[0083] Inventory Turnover Rate: Evaluate inventory turnover speed and identify possible inventory inflation behavior.

[0084] Specific implementation of industry benchmark comparison unit:

[0085] Deviation = (Enterprise Indicator Value - Industry Median) / Industry Standard Deviation

[0086] When the deviation exceeds ±2, it is marked as abnormal.

[0087] Cross-feature generation unit generates new features through feature combination, such as:

[0088] Revenue Growth Rate × Tax Burden Change Rate

[0089] Accounts Receivable Turnover × Gross Profit Margin

[0090] Related Party Transaction Proportion × Industry Concentration

[0091] 3. Risk Assessment Engine

[0092] Risk assessment engine is the core module of the system, including three main units:

[0093] (1) Abnormality detection unit based on deep learning

[0094] Adopt variational autoencoder (VAE) architecture:

[0095] Encoder: 3-layer fully connected network, dimension [1024, 512, 256]

[0096] Latent space: 128 dimensions

[0097] Decoder: Symmetric structure, [256, 512, 1024]

[0098] Loss function: Reconstruction error + KL divergence

[0099] During the training process, the model is trained using transaction data from normal enterprises. In the prediction phase, samples with reconstruction errors exceeding the threshold are identified as anomalies.

[0100] (2) Knowledge graph construction unit

[0101] The entity recognition subunit uses named entity recognition technology to identify the following entity types:

[0102] Enterprise entity: including taxpayer name, tax number

[0103] Personnel entity: legal representative, financial responsible person, tax service personnel

[0104] Related party: upstream and downstream suppliers, customers, related enterprises

[0105] The relationship extraction subunit extracts the following relationship types:

[0106] Equity relationship: holding ratio, actual controller

[0107] Transaction relationship: purchase and sale amount, transaction frequency

[0108] Guarantee relationship: guarantee amount, guarantee period

[0109] The graph embedding subunit uses the Graph2Vec algorithm with the following parameter settings:

[0110] Embedding dimension: 64

[0111] Window size: 5

[0112] Negative sampling number: 10

[0113] (3) Multi-model fusion prediction unit

[0114] Integrate three models for risk prediction:

[0115] Random forest model:

[0116] Number of trees: 500

[0117] Maximum depth: 20

[0118] Minimum leaf node sample size: 50

[0119] XGBoost model:

[0120] Learning rate: 0.01

[0121] Maximum depth: 8

[0122] Subsampling rate: 0.8

[0123] LSTM neural network:

[0124] Hidden layer units: 128

[0125] Number of layers: 2

[0126] Dropout rate: 0.2

[0127] Model fusion uses weighted average method:

[0128] Final risk score = 0.4 × RF_score + 0.35 × XGB_score + 0.25 × LSTM_score

[0129] 4. Intelligent early warning module

[0130] According to the risk score, the system divides the risk into four levels:

[0131] Low risk (0-25 points): green warning, normal monitoring

[0132] Medium risk (26-50 points): yellow warning, pay more attention

[0133] High risk (51-75 points): orange warning, key monitoring

[0134] Extremely high risk (76-100 points): red warning, immediate disposal

[0135] Early warning information is pushed through the following ways:

[0136] System message push

[0137] Email notification

[0138] SMS reminder (above high risk)

[0139] API interface push to other business systems

[0140] 5. Explainability analysis module

[0141] SHAP (SHapley Additive exPlanations) algorithm is used to explain the model prediction results:

[0142] python

[0143] # SHAP value calculation example

[0144] explainer = shap.TreeExplainer(model)

[0145] shap_values = explainer.shap_values(X_test)

[0146] # Generating feature importance report

[0147] feature_importance = pd.DataFrame({

[0148] 'feature': feature_names,

[0149] 'importance': np.abs(shap_values).mean(axis=0)

[0150] }).sort_values('importance', ascending=False)

[0151] The generated risk factor contribution report includes:

[0152] Global feature importance ranking

[0153] Feature contribution decomposition for individual samples

[0154] Feature interaction effect analysis

[0155] 6. Blockchain storage module

[0156] Adopting a consortium chain architecture, the specific implementation is as follows:

[0157] Blockchain platform: Hyperledger Fabric

[0158] Consensus mechanism: PBFT

[0159] Block size: 2MB

[0160] Block time: 3 seconds

[0161] On-chain data includes:

[0162] Risk prediction result summary

[0163] Key decision parameters

[0164] Timestamp and digital signature

[0165] Embodiment 2: Intelligent tax risk prediction method

[0166] This embodiment provides a specific implementation process of an intelligent tax risk prediction method.

[0167] Step S1: Data collection and integration

[0168] Establish a unified data warehouse and use ETL process:

[0169] Extract (extract): Extract raw data from various business systems

[0170] Transform: Data cleaning, format conversion, field mapping

[0171] Load: Load the processed data into the data warehouse.

[0172] The data warehouse adopts a star schema design, with fact tables recording transaction details and dimension tables including time dimension, enterprise dimension, tax type dimension, etc.

[0173] Step S2: Data Preprocessing

[0174] Missing value imputation strategy:

[0175] Numerical variables: use mean interpolation of previous and subsequent values.

[0176] Categorical variables: Fill with mode

[0177] Time series data: using linear interpolation

[0178] Outlier handling:

[0179] IQR method for identifying outliers

[0180] Truncate outliers or replace them with the median.

[0181] Data standardization:

[0182] Python

[0183] # Min-Max Standardization

[0184] X_normalized = (X - X.min()) / (X.max() - X.min())

[0185] # Z-score standardization

[0186] X_standardized = (X - X.mean()) / X.std()

[0187] Step S3: Feature Extraction

[0188] Example of financial indicator feature calculation:

[0189] Python

[0190] # Gross Profit Margin

[0191] gross_profit_margin = (revenue - cost) / revenue * 100

[0192] Net Profit Margin

[0193] net\_profit\_margin = net\_profit / revenue * 100

[0194] Debt-to-Asset Ratio

[0195] debt\_to\_asset\_ratio = total\_debt / total\_assets * 100

[0196] Behavior Pattern Feature Analysis:

[0197] Invoice Frequency: Calculate the mean and standard deviation of monthly invoice numbers

[0198] Declaration Time Regularity: Analyze the distribution of declaration dates to identify abnormal declaration behavior

[0199] Tax Structure: Calculate the proportion of each tax type and compare it with industry standards

[0200] Correlation Network Feature Calculation:

[0201] python

[0202] Network Centrality

[0203] degree\_centrality = nx.degree\_centrality(G)

[0204] Clustering Coefficient

[0205] clustering\_coefficient = nx.clustering(G)

[0206] PageRank Value

[0207] pagerank\_scores = nx.pagerank(G)

[0208] Step S4: Hybrid Prediction Model Construction

[0209] Innovative Model Architecture Implementation:

[0210] First Layer - Transformer Encoder:

[0211] python

[0212] class TransformerEncoder(nn.Module):

[0213] def __init__(self, d_model=512, nhead=8, num_layers=6):

[0214] super().__init__()

[0215] self.encoder = nn.TransformerEncoder(

[0216] nn.TransformerEncoderLayer(d_model, nhead),

[0217] num_layers )

[0219] Second layer - Graph Neural Network:

[0220] python

[0221] class GCN(nn.Module):

[0222] def __init__(self, in_features, hidden_features, out_features):

[0223] super().__init__()

[0224] self.conv1 = GCNConv(in_features, hidden_features)

[0225] self.conv2 = GCNConv(hidden_features, out_features)

[0226] Third layer - Attention Fusion:

[0227] python

[0228] class AttentionFusion(nn.Module):

[0229] def __init__(self, feature_dims):

[0230] super().__init__()

[0231] self.attention = nn.MultiheadAttention(

[0232] embed_dim=sum(feature_dims),

[0233] num_heads=8 )

[0235] Step S5: Risk Assessment Report Generation

[0236] The report template includes the following:

[0237] Basic information of the enterprise

[0238] Risk level assessment (low / medium / high / extremely high)

[0239] Main risk types and scores

[0240] Analysis of key risk indicators

[0241] Risk evolution trend chart

[0242] List of rectification suggestions

[0243] Step S6: Feedback Learning Mechanism

[0244] Implement online learning strategy:

[0245] python

[0246] # Incremental learning def incremental_learning(model, new_data, new_labels):

[0247] optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

[0248] criterion = nn.CrossEntropyLoss()

[0249] for epoch in range(10):

[0250] outputs = model(new_data)

[0251] loss = criterion(outputs, new_labels)

[0252] optimizer.zero_grad()

[0253] loss.backward()

[0254] optimizer.step()

[0255] Step S7: Federated Learning Implementation

[0256] Using FedAvg Algorithm:

[0257] Each participant trains the model on local data

[0258] Upload model parameters to central server

[0259] Server aggregates parameters:

[0260] python

[0261] global_weights = {}for key in local_weights[0].keys():

[0262] global_weights[key] = sum([w[key] * n for w, n in zip(local_weights, data_nums)]) / sum(data_nums)

[0263] Distribute updated global model

[0264] Example 3: System Deployment and Application

[0265] This system has been deployed in a certain provincial tax bureau, covering tax risk monitoring for 100,000 enterprises. The deployment architecture adopts micro-service design:

[0266] Front-end service: Vue.js + Element UI

[0267] API gateway: Spring Cloud Gateway

[0268] Business service: Spring Boot micro-service

[0269] Data storage: MySQL + MongoDB + Redis

[0270] Message queue: RabbitMQ

[0271] Containerization: Docker + Kubernetes

[0272] System running effect:

[0273] Risk identification accuracy: 92.5%

[0274] False positive rate: less than 5%

[0275] Average response time: less than 2 seconds

[0276] Daily data volume: more than 1TB

[0277] Through 6 months of operation, the system successfully identified 1,237 high-risk enterprises, of which 856 were confirmed by tax inspection to have violated regulations, saving the tax department 320 million yuan in tax losses.

[0278] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0279] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claims involved.

[0280] In addition, it should be understood that although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description manner of the specification is only for clarity, those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be properly combined to form other embodiments that those skilled in the art can understand.

Claims

1. An intelligent tax risk prediction system, characterized in that, include: The data acquisition module is used to collect multi-dimensional tax-related data of enterprises in real time, including financial data, invoice data, declaration data, bank statement data and business registration information; The feature engineering module, connected to the data acquisition module, is used to clean and standardize the acquired raw data and extract tax risk feature vectors. Risk assessment engine, including: The deep learning-based anomaly detection unit uses an autoencoder network to identify abnormal transaction patterns. Knowledge graph construction unit: Constructs a network of enterprise relationships and identifies risks associated with related-party transactions; The multi-model fusion prediction unit integrates random forest, XGBoost and LSTM neural network to output a risk score; The intelligent early warning module generates tiered early warning information based on risk scores and pushes it to the relevant management departments; The interpretability analysis module uses the SHAP algorithm to interpret the prediction results and generate a risk factor contribution report. The blockchain evidence storage module is used to store key prediction results and decision-making processes on the blockchain, ensuring the traceability and immutability of the prediction process.

2. The intelligent tax risk prediction system according to claim 1, characterized in that, The feature engineering module also includes: The time-series feature extraction unit is used to calculate the time-series features of the enterprise's tax burden rate, sales-input ratio, and inventory turnover rate. The industry benchmark comparison unit compares the company's indicators with the benchmark values ​​of the same industry and generates a deviation index. The cross-feature generation unit generates higher-order features through feature cross-combination.

3. The intelligent tax risk prediction system according to claim 1, characterized in that, The knowledge graph construction unit specifically includes: The entity recognition subunit identifies entities such as enterprises, legal persons, shareholders, suppliers, and customers; The relationship extraction sub-unit extracts relationships such as equity, transactions, and guarantees between entities; The graph embedding subunit uses the Graph2Vec algorithm to convert the graph structure into a vector representation.

4. A method for intelligent tax risk prediction, characterized in that, Includes the following steps: S1: Collect multi-source heterogeneous tax-related data from enterprises and establish a unified data warehouse; S2: Preprocess the raw data, including missing value imputation, outlier handling, and data standardization; S3: Extract multi-dimensional risk features, including: Financial indicator characteristics: Calculate financial ratios such as gross profit margin, net profit margin, and debt-to-equity ratio; Behavioral pattern characteristics: analyze invoicing frequency, declaration time patterns, and tax type structure; Relationship network characteristics: Construct enterprise relationship graphs and calculate network centrality and clustering coefficients; S4: Construct a hybrid prediction model: Train a supervised learning model using historical labeled data; Employing a novel risk detection model using unsupervised learning; Optimize risk threshold settings through reinforcement learning; S5: Generate a risk assessment report, including risk level, risk type, key risk points, and rectification recommendations.

5. The intelligent tax risk prediction method according to claim 4, characterized in that, The hybrid prediction model in step S4 employs the following innovative architecture: First layer: Use the Transformer encoder to process time-series data and capture long-term dependencies; The second layer uses a graph neural network to process the enterprise relationship network and learn structured features; The third layer: fuses multi-source features through an attention mechanism and adaptively adjusts the feature weights; Output layer: Generates risk scores from 0 to 100 and probability distributions of risk categories.

6. The intelligent tax risk prediction method according to claim 4, characterized in that, Also includes: S6: Establish a feedback learning mechanism to collect tax audit results and update model parameters; S7: Employs a federated learning framework to achieve cross-institutional model optimization while protecting enterprise privacy.

7. The intelligent tax risk prediction method according to claim 4, characterized in that, The types of risk include: Risks include issuing false invoices, tax evasion, related-party transactions, abnormal tax burden, and inaccurate declarations.

8. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by a processor, implements the method described in any one of claims 4-7.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the method of any one of claims 4-7.

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