Financial activity public opinion comprehensive analysis method based on multi-source data fusion

Abstract

The application belongs to the technical field of financial monitoring and data analysis, and specifically relates to a financial activity public opinion comprehensive analysis method based on multi-source data fusion, steps of which are as follows: step one: a data collection and processing module collects information from news report multi-source data for data preprocessing; step two: a multi-source data fusion module converts information features from different data sources into high-dimensional feature vectors through graph convolution network; step three: a risk feature extraction module further processes the fused high-dimensional feature vectors by using a deep neural network to extract potential risk features; step four: a risk assessment and early warning module assesses the risk probability of financial activities through a classification model according to the extracted risk features, and sets a threshold to trigger early warning; and step five: a data security module adopts homomorphic encryption, differential privacy and distributed storage technology for data based on the risk assessment and early warning module to guarantee the security and privacy of data.

Description

Technical Field

[0001] The present invention relates to the technical field of financial monitoring and data analysis, and in particular to a comprehensive analysis method of public opinion on financial activities based on multi-source data fusion. Background Art

[0002] Traditional financial monitoring relies primarily on internal data from financial institutions or a limited amount of publicly available data, making it difficult to comprehensively and promptly identify potential risks of illegal financial activities. With the rise of diverse data sources such as social media, news reports, and government data, effectively leveraging these heterogeneous data sources for comprehensive analysis and improving the accuracy and timeliness of monitoring has become a major challenge for current financial regulation. While some technologies for monitoring illegal financial activities exist, most suffer from the following issues:

[0003] 1. The data source is single and fails to fully integrate multi-source heterogeneous data;

[0004] 2. Risk assessment and early warning mechanisms are not precise enough, lacking deep learning and real-time feedback;

[0005] 3. Lack of data security and privacy protection may lead to the leakage of sensitive data.

[0006] Therefore, there is an urgent need for a new method and technology to improve the efficiency and accuracy of monitoring illegal financial activities and ensure data security through the integration and intelligent analysis of multi-dimensional data.

[0007] In response to the above-mentioned technical defects, a comprehensive analysis method solution for public opinion on financial activities based on multi-source data fusion is proposed. Summary of the Invention

[0008] In order to solve the above problems, the present invention provides the following technical solutions:

[0009] The comprehensive analysis system for public opinion on financial activities based on multi-source data fusion includes a data collection and preprocessing module, a multi-source heterogeneous data fusion module, a risk feature extraction module, a risk assessment and early warning mechanism module, and a data security assurance module. The data collection and preprocessing module collects information from multiple sources such as social media, news reports, and financial regulatory data, and uses this module to clean and standardize the data, remove irrelevant information, and convert it into structured data.

[0010] The multi-source heterogeneous data fusion module includes a graph convolutional network. Based on the data processed by the data acquisition and preprocessing module, the graph convolutional network is used to embed and fuse the information from different data sources and convert it into a unified high-dimensional feature vector.

[0011] The risk feature extraction module includes a deep neural network model. Based on the data fused by the multi-source heterogeneous data fusion module, the risk feature extraction module further processes the fused features based on the deep neural network model to extract risk features related to illegal financial activities;

[0012] The risk assessment and early warning mechanism module includes a classification model (multi-layer perceptron), which assesses risk based on the risk features extracted by the risk feature extraction module, quantifies risk using a classification model (multi-layer perceptron (MLP)), calculates the probability P of illegal financial activities occurring, and triggers an early warning based on a set threshold;

[0013] The data security assurance module adopts homomorphic encryption, differential privacy and distributed storage technology to ensure the security of data and the privacy of privacy.

[0014] Furthermore, the data collection and preprocessing module includes a data collection unit and a preprocessing unit. The data collection unit collects financial public opinion data, social public data, news report data, and social media data from multiple heterogeneous data sources, and defines the collected data as D;

[0015] The pre-processing unit cleans and standardizes the data, removes irrelevant information, and converts it into structured data;

[0016] The pre-processing unit includes data cleaning, standardization, format conversion and feature extraction; the specific processing steps of the pre-processing unit are as follows:

[0017] Step 1: Clean the collected data to remove noise, duplicate data, and irrelevant data;

[0018] Step 2: For text data, it is necessary to perform word segmentation, remove stop words, and stemming to facilitate subsequent feature extraction;

[0019] Step 3: For unstructured data (such as text or images), convert it into structured feature vectors through natural language processing (NLP) or computer vision (CV) technology to ensure that the data can be effectively processed by the model;

[0020] Step 4: Convert each piece of financial event or public opinion information into a feature vector as input for subsequent analysis. Use the TF-IDF (Term Frequency-Inverse Document Frequency) method to convert the text into numerical features, or use image processing methods to extract visual features of the image.

[0021] Step 5: Define the processed data as D'=Preprocess(D).

[0022] Furthermore, the multi-source heterogeneous data fusion unit includes a graph convolutional network, which embeds heterogeneous data from different sources to ensure that they can be effectively compared and fused in the same feature space. The specific analysis formula steps are as follows:

[0023] Z f =GCN(A f ,D f ),Z s =GCN(A s ,D s )

[0024] A f , A s Among them are the adjacency matrix of financial public opinion data and social public data, D f , D s are the characteristic matrices of financial public opinion data and social public data, Z f , Z s It is represented as an embedded representation obtained after processing through a graph convolutional network (GCN).

[0025] Furthermore, the multi-source heterogeneous data fusion unit analyzes the data by embedding heterogeneous data from different sources based on a graph convolutional network, and obtains a unified multi-dimensional representation through weighted fusion:

[0026] X=αZ f +βZ s

[0027] Where α and β are weight coefficients, and α+β=1.

[0028] Furthermore, the risk feature extraction module includes a deep neural network model, which is based on the data fused by the multi-source heterogeneous data fusion module and uses a deep neural network (DNN) method to extract the risk feature from X=αZ f +βZ s The risk feature R associated with illegal financial activities is extracted from the multi-dimensional representation and trusted and secure calculations are performed to ensure the security of sensitive data.

[0029] R = DNN(X)

[0030] Where X represents the feature vector after multi-source data fusion, which contains information from different data sources, DNN(·) represents the deep neural network (DNN) model, and R is the risk feature extracted from the fused feature vector, indicating the risk level of potential illegal financial activities.

[0031] Furthermore, the risk assessment and early warning mechanism module includes a risk assessment unit and an early warning mechanism unit. The risk assessment includes a classification model (multi-layer perceptron), which assesses the risk based on the risk features extracted by the risk feature extraction module, quantifies the risk using the classification model (multi-layer perceptron (MLP)), calculates the probability P of illegal financial activities, and triggers an early warning based on a set threshold. The calculation formula of the classification model is as follows:

[0032] P=(Risk|R)=σ(W T R+b)

[0033] Where σ is the activation function, W and b are the weight and bias of the model respectively, and P = (Risk|R) represents the predicted probability of the risk of illegal financial activities.

[0034] Specifically, the early warning mechanism unit analyzes the risk assessment data as follows:

[0035]

[0036] Where P = (Risk|R) represents the risk probability value output by the risk assessment model, where θ low ,θ hight Indicates the set high-risk and low-risk thresholds, and Alert indicates whether an alert is triggered.

[0037] Furthermore, the specific analysis of the early warning mechanism unit also includes the following steps:

[0038] The early warning mechanism unit has its corresponding three warning levels as green, yellow and red colors Monitor, False and True respectively, and works through the warning levels to ensure that the system can take appropriate actions under different risk levels.

[0039] Furthermore, the data security assurance module includes a homomorphic encryption unit, a differential privacy unit, and a distributed storage and access control unit: the data security assurance module uses homomorphic encryption, differential privacy, and distributed storage technologies based on the data of the risk assessment and early warning mechanism module to ensure data security and privacy;

[0040] The homomorphic encryption unit allows calculations to be performed directly on encrypted data, avoiding exposure of the original data.

[0041] The differential privacy unit ensures that the user's private information is not leaked during the data analysis process by adding noise;

[0042] The distributed storage and access control unit ensures that sensitive data is only accessible to authorized users by distributing and storing data and using a role-based access control (RBAC) mechanism.

[0043] Furthermore, the method for comprehensive analysis of public opinion on financial activities based on multi-source data fusion is characterized in that the steps of using the comprehensive analysis system for public opinion on financial activities based on multi-source data fusion according to any one of claims 1 to 9 are as follows:

[0044] Step 1: Use the data collection and processing module to collect information from multiple sources such as social media, news reports, and financial regulatory data, clean and standardize the data, remove irrelevant information, and convert it into structured data;

[0045] Step 2: Use the multi-source data fusion module to embed features from different data sources through the graph convolutional network (GCN) and convert them into high-dimensional feature vectors;

[0046] Step 3: Use the risk feature extraction module to further process the fused high-dimensional feature vector using a deep neural network to extract potential risk features;

[0047] Step 4: Use the risk assessment and early warning module to evaluate the risk probability of financial activities based on the extracted risk features through a classification model, and trigger an early warning based on the set threshold;

[0048] Step 5: Use the data security module to apply homomorphic encryption, differential privacy and distributed storage technology to the data based on the risk assessment and early warning module to ensure the security of data and the privacy of privacy.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. The present invention's method for comprehensive analysis of public opinion on financial activities based on multi-source data fusion can more comprehensively and accurately identify potential illegal financial activities through multi-source heterogeneous data fusion technology.

[0051] 2. The present invention's method for comprehensive analysis of public opinion on financial activities based on multi-source data fusion supports real-time risk assessment and early warning, and can issue warnings at the early stages of illegal financial activities, thereby helping regulatory authorities take timely measures;

[0052] 3. In the comprehensive analysis method of public opinion on financial activities based on multi-source data fusion, the present invention uses homomorphic encryption and differential privacy technologies during data processing to ensure the security of sensitive data and prevent data leakage;

[0053] 4. The present invention's comprehensive analysis method of public opinion on financial activities based on multi-source data fusion utilizes advanced technologies such as multidimensional learning and graph convolutional networks to deeply explore the potential correlations between different data sources and achieve more refined analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0055] Figure 1 This is a schematic diagram of the overall steps of the comprehensive analysis method of public opinion on financial activities based on multi-source data fusion of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] like Figure 1 As shown in the figure, the comprehensive analysis system of public opinion on financial activities based on multi-source data fusion includes a data collection and preprocessing module, a multi-source heterogeneous data fusion module, a risk feature extraction module, a risk assessment and early warning mechanism module, and a data security assurance module. The data collection and preprocessing module collects information from social media, news reports, and financial regulatory data, and uses this module to clean and standardize the data, remove irrelevant information, and convert it into structured data.

[0058] The multi-source heterogeneous data fusion module includes a graph convolutional network. Based on the data processed by the data acquisition and preprocessing modules, the graph convolutional network is used to embed and fuse the information from different data sources and convert it into a unified high-dimensional feature vector.

[0059] The risk feature extraction module includes a deep neural network model. Based on the data fused by the multi-source heterogeneous data fusion module, the risk feature extraction module further processes the fused features based on the deep neural network model to extract risk features related to illegal financial activities.

[0060] The risk assessment and early warning mechanism module includes a classification model (multi-layer perceptron), which assesses risk based on the risk features extracted by the risk feature extraction module, quantifies risk using a classification model (multi-layer perceptron (MLP)), calculates the probability P of illegal financial activities, and triggers early warnings based on set thresholds;

[0061] The data security assurance module uses homomorphic encryption, differential privacy and distributed storage technologies to ensure the security of data and the privacy of privacy.

[0062] Specifically, the data collection and preprocessing module includes a data collection unit and a preprocessing unit. The data collection unit collects financial public opinion data, social public data, news report data, and social media data from multiple heterogeneous data sources, and defines the collected data as D;

[0063] The preprocessing unit includes data cleaning, standardization, format conversion and feature extraction; the specific processing steps of the preprocessing unit are as follows:

[0064] Step 1: Clean the collected data to remove noise, duplicate data, and irrelevant data;

[0065] Step 2: For text data, it is necessary to perform word segmentation, remove stop words, and stemming to facilitate subsequent feature extraction;

[0066] Step 3: For unstructured data (such as text or images), convert it into structured feature vectors through natural language processing (NLP) or computer vision (CV) technology to ensure that the data can be effectively processed by the model;

[0067] Step 4: Convert each piece of financial event or public opinion information into a feature vector as input for subsequent analysis. Use the TF-IDF (Term Frequency-Inverse Document Frequency) method to convert the text into numerical features, or use image processing methods to extract visual features of the image.

[0068] Step 5: Define the processed data as D'=Preprocess(D).

[0069] By cleaning the data, the data is made consistent, interference factors are removed, the quality and effectiveness of the data are improved, and subsequent feature extraction and analysis are facilitated.

[0070] Specifically, the fusion unit for multi-source heterogeneous data includes a graph convolutional network, which embeds heterogeneous data from different sources to ensure that they can be effectively compared and fused in the same feature space. The specific analysis formula steps are as follows:

[0071] Z f =GCN(A f ,D f ),Z s =GCN(A s ,D s )

[0072] A f , A s Among them are the adjacency matrix of financial public opinion data and social public data, D f , D s are the characteristic matrices of financial public opinion data and social public data, Z f , Zs It is represented as an embedded representation obtained after processing through a graph convolutional network (GCN).

[0073] A f , A s In particular, the adjacency matrix of financial public opinion data and social public data. The adjacency matrix describes the relationship between data sources, for example, the connection or similarity between different data nodes (such as financial events or people in social media);

[0074] D f , D s They are the feature matrices of financial public opinion data and social public data, which contain the attributes or features of each data node (such as text content, risk score, etc.);

[0075] Z f , Z s The representation is an embedded representation obtained by processing it through a graph convolutional network (GCN). GCN is a graph-based neural network that can effectively process complex data with graph structures. After processing by GCN, the data representation is updated based on the relationships between data nodes and their characteristics;

[0076] Through graph convolutional networks, multi-source data (such as financial data and social public data) are converted into high-dimensional embedding representations to capture the complex relationships in the data and provide a basis for subsequent multi-source data fusion.

[0077] Specifically, the multi-source heterogeneous data fusion unit analyzes the data by embedding heterogeneous data from different sources based on the graph convolutional network. The multi-source heterogeneous data fusion unit obtains a unified multi-dimensional representation through weighted fusion:

[0078] X=αZ f +βZ s

[0079] Where α and β are weight coefficients used to control the importance of financial data and social data in the fusion process, and satisfy α + β = 1. X represents the fused feature vector, which is the final multi-source data fusion result, covering information from different data sources, and Z f , Z s It is represented as a high-dimensional feature vector obtained after graph convolutional network processing; information from different data sources is merged into a unified feature vector X through weighted fusion, which provides basic data for subsequent risk assessment and early warning mechanisms.

[0080] Specifically, the risk feature extraction module includes a deep neural network model, which is based on the data fused by the multi-source heterogeneous data fusion module and uses the deep neural network (DNN) method to extract the risk feature from X=αZ f +βZ sThe risk feature R associated with illegal financial activities is extracted from the multi-dimensional representation and trusted and secure calculations are performed to ensure the security of sensitive data.

[0081] R = DNN(X)

[0082] Where X represents the feature vector after multi-source data fusion, which contains information from different data sources, DNN(·) represents the deep neural network (DNN) model, and R is the risk feature extracted from the fused feature vector, indicating the risk level of potential illegal financial activities.

[0083] Deep neural networks extract risk features related to illegal financial activities by training and reasoning on multi-source fusion data. These features may include abnormal patterns of financial transactions and risk signals in social media.

[0084] Specifically, the risk assessment and early warning mechanism module includes a risk assessment unit and an early warning mechanism unit. The risk assessment includes a classification model (multi-layer perceptron), which assesses the risk based on the risk features extracted by the risk feature extraction module, quantifies the risk using a classification model (multi-layer perceptron (MLP)), calculates the probability P of illegal financial activities, and triggers an early warning based on the set threshold. The calculation formula of the classification model is as follows:

[0085] P=(Risk|R)=σ(W T R+b)

[0086] Where σ is the activation function, typically using functions such as Sigmoid or ReLU, to ensure that the output value is within a specified range. W and b are the weight and bias of the model, respectively. W contains the parameters learned in the neural network or classification model, and b helps the model perform translation adjustments. P = (Risk|R) represents the predicted probability of the risk of illegal financial activities, which represents the probability of illegal financial activities occurring given the feature R. R is the risk feature vector extracted from the fused feature vector, which contains the risk features related to illegal financial activities extracted after multi-source data fusion.

[0087] Formulate a typical classification problem, where the extracted risk feature R is converted into a probability value P = (Risk|R) = σ(W T R+b) represents the probability of an event occurring.

[0088] Specifically, the early warning mechanism unit analyzes the risk assessment data as follows:

[0089]

[0090] Where P = (Risk|R) represents the risk probability value output by the risk assessment model, where θ low ,θ hight Indicates the set high-risk and low-risk thresholds. Alert indicates whether an alert is triggered. Monitor, False, and True indicate continuous monitoring, no alert, and triggering of an alert, respectively.

[0091] Specifically, the early warning mechanism unit analysis also includes the following steps:

[0092] The early warning mechanism unit assigns its corresponding three warning levels to three colors: Monitor, False, and True, respectively, in green, yellow, and red. It works through the warning levels to ensure that the system can take appropriate actions at different risk levels;

[0093] When the risk probability is greater than the high risk threshold θ hight , the system triggers the emergency warning Alert = True, and the red light turns on;

[0094] When the risk probability is between low risk and high risk, the system maintains the monitoring state Alert = Monitor and the green light is on;

[0095] When the risk probability is less than the low risk threshold θ low When it is considered that there is no risk, Alert = False, the system does not trigger the warning, and the yellow light is on;

[0096] The early warning mechanism ensures that the system can take appropriate actions at different risk levels by setting different thresholds.

[0097] Specifically, the data security assurance module includes a homomorphic encryption unit, a differential privacy unit, and a distributed storage and access control unit: the data security assurance module is based on the data of the risk assessment and early warning mechanism module, and adopts homomorphic encryption, differential privacy, and distributed storage technologies to ensure data security and privacy;

[0098] Homomorphic encryption unit: allows calculations to be performed directly on encrypted data, avoiding exposure of the original data;

[0099] Differential privacy unit: By adding noise, it ensures that the user's private information is not leaked during the data analysis process;

[0100] Distributed storage and access control unit: By distributing data storage and using a role-based access control (RBAC) mechanism, sensitive data is ensured to be accessible only to authorized users.

[0101] Specifically, the method for comprehensive analysis of public opinion on financial activities based on multi-source data fusion adopts the system for comprehensive analysis of public opinion on financial activities based on multi-source data fusion described in any one of claims 1 to 9, and the steps are as follows:

[0102] Step 1: Use the data collection and processing module to collect information from multiple sources such as social media, news reports, and financial regulatory data, clean and standardize the data, remove irrelevant information, and convert it into structured data;

[0103] Step 2: Use the multi-source data fusion module to embed features from different data sources through the graph convolutional network (GCN) and convert them into high-dimensional feature vectors;

[0104] Step 3: Use the risk feature extraction module to further process the fused high-dimensional feature vector using a deep neural network to extract potential risk features;

[0105] Step 4: Use the risk assessment and early warning module to evaluate the risk probability of financial activities based on the extracted risk features through a classification model, and trigger an early warning based on the set threshold;

[0106] Step 5: Use the data security module to apply homomorphic encryption, differential privacy and distributed storage technology to the data based on the risk assessment and early warning module to ensure the security of data and the privacy of privacy.

[0107] Working Principle: The data collection and processing module collects information D from multiple sources, including social media, news reports, and financial regulatory data. The data is cleaned and standardized to remove irrelevant information and converted into structured data D' = Preprocess(D). The multi-source data fusion module then uses a graph convolutional network (GCN) to embed features from the information from different data sources and convert it into a high-dimensional feature vector. The specific analysis formula steps are as follows:

[0108] Z f =GCN(A f ,D f ),Z s =GCN(A s ,D s )

[0109] A f , A s Among them are the adjacency matrix of financial public opinion data and social public data, D f , D s are the characteristic matrices of financial public opinion data and social public data, Z f , Z sThe fusion unit of multi-source heterogeneous data represents the embedding representation obtained by processing through a graph convolution network (GCN), and analyzes the embedding representation data of heterogeneous data from different sources based on the graph convolution network. The fusion unit of multi-source heterogeneous data obtains a unified multi-dimensional representation through weighted fusion:

[0110] X = aZ + bZ f s

[0111] where a and b are weight coefficients, and a + b = 1;

[0112] Then the risk feature extraction module uses a deep neural network to further process the fused high-dimensional feature vector, extracts potential risk features, and ensures the security of sensitive data, where

[0113] R = DNN(X)

[0114] Then the risk assessment and early warning module assesses the risk probability of financial activities according to the extracted risk features through a classification model, and triggers an early warning according to the set threshold, P = (Risk | R) = σ (W T R + b)

[0115] where σ is an activation function, W and b are the weight and bias of the model respectively, and P = (Risk | R) represents the prediction probability of illegal financial activity risk. The early warning mechanism unit analyzes the data based on risk assessment as follows:

[0116]

[0117] where P = (Risk | R) represents the risk probability value output by the risk assessment model, and θ low and θ hight represent the set high-risk and low-risk thresholds, and Alert represents the identification of whether to trigger an early warning. The early warning mechanism unit has three corresponding early warning levels, namely green, yellow and red colors Monitor, False and True, which work through the early warning level to ensure that the system can take appropriate action at different risk levels. Then the data security module uses homomorphic encryption, differential privacy and distributed storage technology based on the data of the risk assessment and early warning module to ensure the security and privacy of the data.

[0118] ​The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A comprehensive analysis system of public opinion on financial activities based on multi-source data fusion, characterized by: It includes a data collection and preprocessing module, a multi-source heterogeneous data fusion module, a risk feature extraction module, a risk assessment and early warning mechanism module, and a data security assurance module. The data collection and preprocessing module collects public opinion information related to financial activities from multiple sources such as social media, news reports, and financial regulatory data, and uses this module to clean and standardize the data, remove irrelevant information, and convert it into structured data. The multi-source heterogeneous data fusion module includes a graph convolutional network (GCN). Based on the data processed by the data collection and preprocessing module, the GCN performs feature embedding and fusion on the financial public opinion graph structure data (including user-topic-event association relationships) from different data sources, converting it into a unified high-dimensional feature vector to ensure that it can be effectively compared and fused in the same feature space. The specific analysis formula steps are as follows: Z f =GCN(A f ,D f ),Z s =GCN(A s ,D s ) A f , A s Among them are the adjacency matrix of financial public opinion data and social public data, D f , D s are the characteristic matrices of financial public opinion data and social public data, Z f , Z s Represented as the embedded representation obtained after processing by the graph convolutional network (GCN); The multi-source heterogeneous data fusion unit analyzes the data by embedding heterogeneous data from different sources based on a graph convolutional network. The multi-source heterogeneous data fusion unit obtains a unified multi-dimensional representation through weighted fusion: X=αZ f +βZ s Where α and β are weight coefficients, and α+β=1; The risk feature extraction module includes a deep neural network model, which is based on the data fused by the multi-source heterogeneous data fusion module and uses a deep neural network (DNN) method to extract the risk feature from X=αZ f +βZ s The risk feature R associated with illegal financial activities is extracted from the multi-dimensional representation and trusted and secure calculations are performed to ensure the security of sensitive data. R = DNN(X) Where X represents the feature vector after multi-source data fusion, which contains information from different data sources, DNN(·) represents the deep neural network (DNN) model, and R is the risk feature extracted from the fused feature vector, indicating the risk level of potential illegal financial activities. The risk assessment and early warning mechanism module includes a risk assessment unit and an early warning mechanism unit. The risk assessment includes a classification model (multi-layer perceptron), which assesses risk based on the risk features extracted by the risk feature extraction module, quantifies risk using a classification model (multi-layer perceptron (MLP)), calculates the probability P of illegal financial activities, and triggers an early warning based on a set threshold. The calculation formula of the classification model is as follows: P=(Risk|R)=σ(W T R+b) Where σ is the activation function, W and b are the weight and bias of the model respectively, and P = (Risk|R) represents the predicted probability of the risk of illegal financial activities; The early warning mechanism unit specifically analyzes the risk assessment data as follows: Where P = (Risk|R) represents the risk probability value output by the risk assessment model, where θ low ,θ hight Indicates the set high-risk and low-risk thresholds, Alert indicates whether an alert is triggered, and Monitor, False, and True indicate continuous monitoring, no alert, and triggering of an alert, respectively.

2. The comprehensive analysis system of public opinion on financial activities based on multi-source data fusion according to claim 1 is characterized in that: The data collection and preprocessing module includes a data collection unit and a preprocessing unit. The data collection unit collects financial public opinion data, social public data, news report data, and social media data from multiple heterogeneous data sources, and defines the collected data as D; The pre-processing unit cleans and standardizes the data, removes irrelevant information, and converts it into structured data; The pre-processing unit includes data cleaning, standardization, format conversion and feature extraction; the specific processing steps of the pre-processing unit are as follows: Step 1: Clean the collected data to remove noise, duplicate data, and irrelevant data; Step 2: For text data, it is necessary to perform word segmentation, remove stop words, and stemming to facilitate subsequent feature extraction; Step 3: For unstructured data (such as text or images), convert it into structured feature vectors through natural language processing (NLP) or computer vision (CV) technology to ensure that the data can be effectively processed by the model; Step 4: Convert each piece of financial event or public opinion information into a feature vector as input for subsequent analysis. Use the TF-IDF (Term Frequency-Inverse Document Frequency) method to convert the text into numerical features, or use image processing methods to extract visual features of the image. Step 5: Define the processed data as D'=Preprocess(D).

3. The comprehensive analysis system of public opinion on financial activities based on multi-source data fusion according to claim 1 is characterized in that: The specific analysis of the early warning mechanism unit also includes the following steps: The early warning mechanism unit has its corresponding three warning levels as green, yellow and red colors Monitor, False and True respectively, and works through the warning levels to ensure that the system can take appropriate actions under different risk levels.

4. The comprehensive analysis system of public opinion on financial activities based on multi-source data fusion according to claim 1 is characterized in that: The data security assurance module includes a homomorphic encryption unit, a differential privacy unit, and a distributed storage and access control unit: the data security assurance module is based on the data of the risk assessment and early warning mechanism module and adopts a collaborative solution of homomorphic encryption, differential privacy, and distributed storage technology to ensure the security of data and the privacy of privacy; The homomorphic encryption unit allows risk feature extraction and assessment calculations to be performed directly on encrypted data, avoiding exposure of the original data. The differential privacy unit adds dynamic Gaussian noise to ensure that the user's private information is not leaked during the data analysis process, and the noise intensity is dynamically adjusted according to the data sensitivity; The distributed storage and access control unit ensures that sensitive data is only accessible to authorized users and that all data operations generate blockchain audit logs by distributing and storing data across more than five nodes and using a role-based access control (RBAC) mechanism to bind permissions to warning levels.

5. The method for comprehensive analysis of public opinion on financial activities based on multi-source data fusion according to claim 1 is characterized in that: The steps of using the comprehensive analysis system of public opinion on financial activities based on multi-source data fusion as described in any one of claims 1 to 4 are as follows: Step 1: Use the data collection and processing module to collect information from multiple sources such as social media, news reports, and financial regulatory data, clean and standardize the data, remove irrelevant information, and convert it into structured data; Step 2: Use the multi-source data fusion module to embed features from different data sources through the graph convolutional network (GCN) and convert them into high-dimensional feature vectors; Step 3: Use the risk feature extraction module to further process the fused high-dimensional feature vector using a deep neural network to extract potential risk features; Step 4: Use the risk assessment and early warning module to evaluate the risk probability of financial activities based on the extracted risk features through a classification model, and trigger an early warning based on the set threshold; Step 5: Use the data security module to apply homomorphic encryption, differential privacy and distributed storage technology to the data based on the risk assessment and early warning module to ensure the security of data and the privacy of privacy.

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