Marketing business risk prevention and control method for spot transaction scene

Abstract

The invention relates to the field of marketing risk prevention and control, in particular to a marketing business risk prevention and control method for spot transaction scenes. The technical problem is that when risk prevention and control are carried out on marketing business in a spot transaction scene, market research and analysis are carried out on a market by collecting transaction data on the market so as to understand a market trend and a strategy of a competitor as a main index for risk prevention and control, and only the current condition of the market is considered. The market is difficult to comprehensively analyze and research; according to the technical scheme, the marketing business risk prevention and control method for the spot transaction scene comprises the steps of data processing and identification, external risk analysis and internal process risk analysis; by setting the preset specific data interface, the prediction data of the market development can be input, so that the market development can be effectively predicted, the market development can be combined with the current condition of the market, the market demand can be effectively analyzed and predicted, and effective data support is provided for risk prevention and control.

Description

Technical Field

[0001] The present invention relates to the field of marketing risk prevention and control, and in particular to a marketing business risk prevention and control method for spot trading scenarios. Background Art

[0002] The marketing business risk prevention and control method in the spot trading scenario should be an effective way; in this method, we need to focus on some key factors to ensure that risks are effectively controlled; first, we should establish a strict market research and analysis system to understand market trends and competitors' strategies; this can help us assess risks more accurately in the decision-making process and take corresponding measures to meet market challenges; in addition, we need to ensure that our sales staff have sufficient professional knowledge and skills to cope with a variety of different market situations; this includes providing training and continuing education opportunities to ensure that they can adapt to market changes and effectively market our products; in addition, we also need to establish a strong customer relationship management system to better understand customer needs and provide personalized services; this helps to improve customer satisfaction and reduce the risks brought about by meeting customer needs; finally, we also need to develop a set of strict risk control measures and processes to ensure that we can promptly detect and prevent any potential business risks; this includes regularly reviewing our business processes and internal controls to ensure that they remain effective and making adjustments and improvements as needed; by following this risk prevention and control method for marketing business in spot trading scenarios, we can better cope with risks and ensure that our business can develop steadily; when conducting risk prevention and control for marketing business within spot trading scenarios, market research and analysis of the market is conducted by collecting transaction data on the market to understand market trends and competitors' strategies. As the main indicator of risk prevention and control, only the current situation of the market is taken into account, and it is difficult to conduct a comprehensive analysis and research on the market. Summary of the invention

[0003] In order to overcome the problem that when conducting risk prevention and control for marketing business within the spot trading scenario, market research and analysis of the market is carried out by collecting transaction data on the market to understand market trends and competitor strategies. As the main indicator for risk prevention and control, only the current situation of the market is considered, making it difficult to conduct a comprehensive analysis and research on the market.

[0004] The technical solution of the present invention is: a marketing business risk prevention and control method for spot trading scenarios, comprising the following steps:

[0005] S11: Collect relevant data of the marketing business generated in the marketing business of spot trading using the set data collection method;

[0006] S12: Data processing and identification, using different processing methods to process the collected marketing business related data, and identifying the processed marketing business related data;

[0007] S13: External risk analysis: Analyze the external risks of marketing business based on the identification results of relevant data of marketing business;

[0008] S14: Internal process risk analysis: Analyze the internal risks of marketing business based on the identification results of relevant data of marketing business;

[0009] S15: Risk management: Based on the results of external risk analysis and internal process risk analysis, different methods are selected to manage risks in marketing operations;

[0010] Preferably, data related to the marketing business is collected, and different processing methods are used to process the collected marketing business related data, and the processed marketing business related data is identified, and then the data is divided into external risk analysis data and internal risk analysis data according to the difference of the data, and then different processing is performed separately according to the analysis results of the external risk analysis data and the internal risk analysis data, so that different risk prevention and control processing processes can be carried out for different situations, ensuring that the risk prevention and control is more comprehensive.

[0011] As a preferred embodiment, when collecting the marketing business related data generated in the marketing business of spot education by using the set data collection method, the marketing business related data is collected by the following method:

[0012] A11: Collect text chat records generated during relevant marketing activities or spot transactions on the Internet;

[0013] A12: Collect voice chat records generated during relevant marketing activities or spot transactions on the Internet;

[0014] A13: Collect materials such as agreements or contracts signed with customers.

[0015] Preferably, when collecting relevant data of the marketing business generated in the marketing business of spot education using the set data collection method, the relevant data of the marketing business include: information on the names and types of goods involved in relevant marketing activities or spot transactions on the Internet, price information on the goods involved in relevant marketing activities or spot transactions on the Internet, quantity information on the goods involved in relevant marketing activities or spot transactions on the Internet, and time information involved in the marketing business, etc., wherein the time information involved in the marketing business includes the time information of the relevant marketing activities or spot transactions on the Internet and the transaction time of the relevant marketing activities or spot transactions on the Internet.

[0016] Preferably, when collecting the marketing business-related data generated in the marketing business of spot education using the set data collection method, the marketing business-related data also includes: specific information input through a pre-set interface and customer evaluation information.

[0017] Preferably, when collecting the relevant data of the marketing business generated in the marketing business of spot education by using the set data collection method, the following steps are included:

[0018] S21: Using methods A11-A13, collect marketing business related data generated during relevant marketing activities or spot transactions on the Internet, and store them on a local smart device or other storage device;

[0019] S22: pre-processing the collected marketing business related data on the local device;

[0020] S23: Send the pre-processed marketing business related data to the cloud server for subsequent processing.

[0021] Preferably, when processing the collected marketing business related data and identifying the processed marketing business related data, when processing the commodity name and category information involved in the relevant marketing activities or spot transactions on the network, the price information of the commodities involved in the relevant marketing activities or spot transactions on the network, the quantity information of the commodities involved in the relevant marketing activities or spot transactions on the network, and the time information involved in the marketing business, the following steps are included:

[0022] S31: using a variety of data recognition algorithms to recognize the data collected in step S21, and converting the data in various formats into a unified data format;

[0023] S32: using a convolutional neural network algorithm to perform feature recognition on the data converted into a unified data format, and using a classification algorithm based on a decision tree to classify the data;

[0024] S33: Arrange and summarize the data according to the classification of the data, and organize the arranged and summarized data into a preset table format.

[0025] Preferably, by collecting relevant information of spot transactions collected on the Internet, it is possible to understand information such as consumer demand in the target market, competitor strategies, and market trends, so that enterprises can have a more comprehensive and thorough understanding of market development information and trends, which can help enterprises predict market risks and help enterprises formulate reasonable risk prevention and control plans and risk handling plans to ensure that risk prevention and control are timely and accurate.

[0026] Preferably, when processing the collected marketing business related data and identifying the processed marketing business related data, when processing the specific information input through a preset interface, the following steps are included:

[0027] S41: Using a text classification algorithm based on a convolutional neural network to perform feature recognition and label classification on specific information input through a pre-set interface in combination with a preset risk classification method;

[0028] S42: Using a classification method based on a deep learning method to judge the feature recognition results and label classification results.

[0029] Preferably, when processing the collected marketing business related data and identifying the processed marketing business related data, when processing the customer's evaluation information, the following steps are included:

[0030] S51: removing some modal particles, common prepositions, punctuation marks and low-frequency words from the text information related to the evaluation information, verifying and cleaning the data, and removing noise data and interfering text from the data;

[0031] S52: segmenting the text information after removing the invalid data using a neural network word segmentation method, segmenting the sentences in the text information into words one by one, and obtaining an evaluation vocabulary group containing all evaluation information;

[0032] S53: removing irrelevant words in the evaluation vocabulary group, wherein the irrelevant words include dates, quantifiers, and verbs;

[0033] S54: All synonyms and antonyms in the evaluation vocabulary group are merged and sorted according to the frequency of occurrence to obtain an evaluation keyword group with frequency information.

[0034] Preferably, when analyzing the external risks of the marketing business according to the identification results of the relevant data of the marketing business, the following steps are included:

[0035] S61: extracting the information in the table obtained in step S33, and extracting market change information such as commodity transaction volume change information and commodity price change information;

[0036] S62: Based on market change information such as commodity transaction volume change information and commodity price change information, a risk category determination is performed using a preset risk classification method;

[0037] S63: Send the determined risk category, the extracted commodity transaction volume change information, commodity price change information and other market change information to the auditor for review.

[0038] Preferably, when analyzing the external risks of the marketing business according to the identification results of the relevant data of the marketing business, the following steps are also included:

[0039] S71: extracting the information in the table obtained in step S33, and extracting the recognition result of the specific information input through the preset interface;

[0040] S72: According to a preset risk classification method, a risk classification is determined for the identification result of the specific information input through a preset interface;

[0041] S73: According to pre-set rules, the risk levels obtained in steps S62 and S72 are combined to obtain a final risk category determination result.

[0042] Preferably, by classifying specific information input through a pre-set interface using a classification method based on a deep learning method, market-related information can be effectively analyzed, thereby achieving an effective prediction of market development, which can be combined with the current market situation to effectively analyze and predict market demand, provide accurate and effective data support for marketing strategies, and enable more accurate risk assessment in the decision-making process.

[0043] Preferably, when analyzing the internal risks of the marketing business according to the identification results of the relevant data of the marketing business, the following steps are included:

[0044] S81: searching in a pre-set risk question library according to the evaluation keyword group with frequency information to obtain the risk question corresponding to each evaluation keyword;

[0045] S82: Score each evaluation keyword group according to the frequency information and the corresponding risk issues;

[0046] S83: using a weighted average method to synthesize the scores of all evaluation keyword groups;

[0047] S84: Based on the set rules, different risk management methods are called up according to different scores.

[0048] Preferably, by processing customer evaluation information, we can more thoroughly understand customer demand information and customer satisfaction information and conduct timely investigation and processing, which can effectively help establish a good customer relationship management system, effectively prevent risks arising from normal marketing activities, and ensure the rationality of the internal management system.

[0049] Beneficial effects of the present invention:

[0050] 1. Compared with the existing technology, when conducting risk prevention and control for marketing business in spot trading scenarios, the market research and analysis is carried out by collecting transaction data on the market to understand market trends and competitors' strategies. As the main indicator of risk prevention and control, only the current situation of the market is considered, and it is difficult to conduct a comprehensive analysis and research on the market; this risk prevention and control method can input the forecast data of market development by setting a preset specific data interface, so that the development of the market can be effectively predicted, and it can be combined with the current situation of the market to effectively analyze and predict the market demand, providing effective data support for risk prevention and control;

[0051] 2. By classifying specific information input through a pre-set interface using a classification method based on deep learning methods, market-related information can be effectively analyzed, thereby achieving effective predictions of market development, and thus combining it with the current market situation to effectively analyze and predict market demand, providing accurate and effective data support for marketing strategies, and enabling more accurate risk assessment in the decision-making process;

[0052] 3. By collecting relevant information about spot transactions collected on the Internet, we can understand the consumer demand of the target market, the strategies of competitors, market trends and other information, so that enterprises can have a more comprehensive and thorough understanding of market development information and trends, which can help enterprises predict market risks and help enterprises formulate reasonable risk prevention and control plans and risk handling plans to ensure timely and accurate risk prevention and control.

[0053] 4. By processing customer evaluation information, we can more thoroughly understand customer demand information and customer satisfaction information and conduct timely investigation and processing, which can effectively help establish a good customer relationship management system, effectively prevent risks arising from normal marketing activities, and ensure the rationality of the internal management system;

[0054] 5. By collecting data related to marketing business, using different processing methods to process the collected marketing business related data, and identifying the processed marketing business related data, the data is divided into external risk analysis data and internal risk analysis data according to the difference of data, and then different processing is performed separately according to the analysis results of external risk analysis data and internal risk analysis data, so that different risk prevention and control processing processes can be carried out for different situations to ensure more comprehensive risk prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 What is shown is a flow chart of the marketing business risk prevention and control method for spot trading scenarios of the present invention;

[0056] Figure 2 What is shown is a flow chart of external risk analysis in the marketing business risk prevention and control method for spot trading scenarios of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0058] See also Figure 1 The present invention provides an embodiment: a marketing business risk prevention and control method for spot trading scenarios, comprising the following steps:

[0059] S11: Collect relevant data of the marketing business generated in the marketing business of spot trading using the set data collection method;

[0060] S12: Data processing and identification, using different processing methods to process the collected marketing business related data, and identifying the processed marketing business related data;

[0061] S13: External risk analysis: Analyze the external risks of marketing business based on the identification results of relevant data of marketing business;

[0062] S14: Internal process risk analysis: Analyze the internal risks of marketing business based on the identification results of relevant data of marketing business;

[0063] S15: Risk management: Based on the results of external risk analysis and internal process risk analysis, different methods are selected to handle risks for marketing operations.

[0064] Preferably, data related to the marketing business is collected, and different processing methods are used to process the collected marketing business related data, and the processed marketing business related data is identified, and then the data is divided into external risk analysis data and internal risk analysis data according to the difference of the data, and then different processing is performed separately according to the analysis results of the external risk analysis data and the internal risk analysis data, so that different risk prevention and control processing processes can be carried out for different situations, ensuring that the risk prevention and control is more comprehensive.

[0065] As a preferred embodiment, when collecting the marketing business related data generated in the marketing business of spot education by using the set data collection method, the marketing business related data is collected by the following method:

[0066] A11: Collect text chat records generated during relevant marketing activities or spot transactions on the Internet;

[0067] A12: Collect voice chat records generated during relevant marketing activities or spot transactions on the Internet;

[0068] A13: Collect materials such as agreements or contracts signed with customers.

[0069] Preferably, when collecting relevant data of the marketing business generated in the marketing business of spot education using the set data collection method, the relevant data of the marketing business include: information on the names and types of goods involved in relevant marketing activities or spot transactions on the Internet, price information on the goods involved in relevant marketing activities or spot transactions on the Internet, quantity information on the goods involved in relevant marketing activities or spot transactions on the Internet, and time information involved in the marketing business, etc., wherein the time information involved in the marketing business includes the time information of the relevant marketing activities or spot transactions on the Internet and the transaction time of the relevant marketing activities or spot transactions on the Internet.

[0070] Preferably, when collecting the marketing business-related data generated in the marketing business of spot education using the set data collection method, the marketing business-related data also includes: specific information input through a pre-set interface and customer evaluation information.

[0071] Preferably, when collecting the relevant data of the marketing business generated in the marketing business of spot education by using the set data collection method, the following steps are included:

[0072] S21: Using methods A11-A13, collect marketing business related data generated during relevant marketing activities or spot transactions on the Internet, and store them on a local smart device or other storage device;

[0073] S22: pre-processing the collected marketing business related data on the local device;

[0074] S23: Send the pre-processed marketing business related data to the cloud server for subsequent processing.

[0075] Preferably, when processing the collected marketing business related data and identifying the processed marketing business related data, when processing the commodity name and category information involved in the relevant marketing activities or spot transactions on the network, the price information of the commodities involved in the relevant marketing activities or spot transactions on the network, the quantity information of the commodities involved in the relevant marketing activities or spot transactions on the network, and the time information involved in the marketing business, the following steps are included:

[0076] S31: using a variety of data recognition algorithms to recognize the data collected in step S21, and converting the data in various formats into a unified data format;

[0077] S32: using a convolutional neural network algorithm to perform feature recognition on the data converted into a unified data format, and using a classification algorithm based on a decision tree to classify the data;

[0078] S33: Arrange and summarize the data according to the classification of the data, and organize the arranged and summarized data into a preset table format.

[0079] Preferably, by collecting relevant information of spot transactions collected on the Internet, it is possible to understand information such as consumer demand in the target market, competitor strategies, and market trends, so that enterprises can have a more comprehensive and thorough understanding of market development information and trends, which can help enterprises predict market risks and help enterprises formulate reasonable risk prevention and control plans and risk handling plans to ensure that risk prevention and control are timely and accurate.

[0080] See also Figure 2 In this embodiment, when the collected marketing business related data is processed and the processed marketing business related data is identified, when processing the specific information input through the preset interface, the following steps are included:

[0081] S41: Using a text classification algorithm based on a convolutional neural network to perform feature recognition and label classification on specific information input through a pre-set interface in combination with a preset risk classification method;

[0082] S42: Using a classification method based on a deep learning method to judge the feature recognition results and label classification results.

[0083] Preferably, when processing the collected marketing business related data and identifying the processed marketing business related data, when processing the customer's evaluation information, the following steps are included:

[0084] S51: removing some modal particles, common prepositions, punctuation marks and low-frequency words from the text information related to the evaluation information, verifying and cleaning the data, and removing noise data and interfering text from the data;

[0085] S52: segmenting the text information after removing the invalid data using a neural network word segmentation method, segmenting the sentences in the text information into words one by one, and obtaining an evaluation vocabulary group containing all evaluation information;

[0086] S53: removing irrelevant words in the evaluation vocabulary group, wherein the irrelevant words include dates, quantifiers, and verbs;

[0087] S54: All synonyms and antonyms in the evaluation vocabulary group are merged and sorted according to the frequency of occurrence to obtain an evaluation keyword group with frequency information.

[0088] Preferably, when analyzing the external risks of the marketing business according to the identification results of the relevant data of the marketing business, the following steps are included:

[0089] S61: extracting the information in the table obtained in step S33, and extracting market change information such as commodity transaction volume change information and commodity price change information;

[0090] S62: Based on market change information such as commodity transaction volume change information and commodity price change information, a risk category determination is performed using a preset risk classification method;

[0091] S63: Send the determined risk category, the extracted commodity transaction volume change information, commodity price change information and other market change information to the auditor for review.

[0092] Preferably, when analyzing the external risks of the marketing business according to the identification results of the relevant data of the marketing business, the following steps are also included:

[0093] S71: extracting the information in the table obtained in step S33, and extracting the recognition result of the specific information input through the preset interface;

[0094] S72: According to a preset risk classification method, a risk classification is determined for the identification result of the specific information input through a preset interface;

[0095] S73: According to pre-set rules, the risk levels obtained in steps S62 and S72 are combined to obtain a final risk category determination result.

[0096] Preferably, by classifying specific information input through a pre-set interface using a classification method based on a deep learning method, market-related information can be effectively analyzed, thereby achieving an effective prediction of market development, which can be combined with the current market situation to effectively analyze and predict market demand, provide accurate and effective data support for marketing strategies, and enable more accurate risk assessment in the decision-making process.

[0097] Preferably, when analyzing the internal risks of the marketing business according to the identification results of the relevant data of the marketing business, the following steps are included:

[0098] S81: searching in a pre-set risk question library according to the evaluation keyword group with frequency information to obtain the risk question corresponding to each evaluation keyword;

[0099] S82: Score each evaluation keyword group according to the frequency information and the corresponding risk issues;

[0100] S83: using a weighted average method to synthesize the scores of all evaluation keyword groups;

[0101] S84: Based on the set rules, different risk management methods are called up according to different scores.

[0102] Preferably, by processing customer evaluation information, we can more thoroughly understand customer demand information and customer satisfaction information and conduct timely investigation and processing, which can effectively help establish a good customer relationship management system, effectively prevent risks arising from normal marketing activities, and ensure the rationality of the internal management system.

[0103] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. A marketing business risk prevention and control method for spot trading scenarios; characterized by: The following steps are included: S11: Collect relevant data of the marketing business generated in the marketing business of spot trading using the set data collection method; S12: Data processing and identification, using different processing methods to process the collected marketing business related data, and identifying the processed marketing business related data; S13: External risk analysis: Analyze the external risks of marketing business based on the identification results of relevant data of marketing business; S14: Internal process risk analysis: Analyze the internal risks of marketing business based on the identification results of relevant data of marketing business; S15: Risk management: Based on the results of external risk analysis and internal process risk analysis, different methods are selected to handle risks for marketing operations.

2. A marketing business risk prevention and control method for spot trading scenarios according to claim 1, characterized in that: When collecting the marketing business-related data generated in the marketing business of Spot Education using the set data collection method, the marketing business-related data is collected through the following methods: A11: Collect text chat records generated during relevant marketing activities or spot transactions on the Internet; A12: Collect voice chat records generated during relevant marketing activities or spot transactions on the Internet; A13: Collect materials such as agreements or contracts signed with customers.

3. A marketing business risk prevention and control method for spot trading scenarios according to claim 2, characterized in that: When collecting relevant data on the marketing business of spot education using the set data collection method, the relevant data on the marketing business include: information on the names and types of goods involved in relevant marketing activities or spot transactions on the Internet, price information on goods involved in relevant marketing activities or spot transactions on the Internet, quantity information on goods involved in relevant marketing activities or spot transactions on the Internet, and time information involved in the marketing business, etc., wherein the time information involved in the marketing business includes time information on the occurrence of relevant marketing activities or spot transactions on the Internet and transaction time of relevant marketing activities or spot transactions on the Internet, etc. When collecting relevant data on the marketing business of spot education using the set data collection method, the relevant data on the marketing business also include: specific information input through a pre-set interface and customer evaluation information.

4. A marketing business risk prevention and control method for spot trading scenarios according to claim 3, characterized in that: When collecting the relevant data of the marketing business generated in the marketing business of Spot Education using the set data collection method, the following steps are included: S21: Using methods A11-A13, collect marketing business related data generated during relevant marketing activities or spot transactions on the Internet, and store them on a local smart device or other storage device; S22: pre-processing the collected marketing business related data on the local device; S23: Send the pre-processed marketing business related data to the cloud server for subsequent processing.

5. A marketing business risk prevention and control method for spot trading scenarios according to claim 4, characterized in that: When processing the collected data related to marketing business and identifying the processed data related to marketing business, when processing information such as the name and type of goods involved in the relevant marketing activities or spot transactions on the Internet, the price information of the goods involved in the relevant marketing activities or spot transactions on the Internet, the quantity information of the goods involved in the relevant marketing activities or spot transactions on the Internet, and the time information involved in the marketing business, the following steps are included: S31: using a variety of data recognition algorithms to recognize the data collected in step S21, and converting the data in various formats into a unified data format; S32: using a convolutional neural network algorithm to perform feature recognition on the data converted into a unified data format, and using a classification algorithm based on a decision tree to classify the data; S33: Arrange and summarize the data according to the classification of the data, and organize the arranged and summarized data into a preset table format.

6. A marketing business risk prevention and control method for spot trading scenarios according to claim 5, characterized in that: When processing the collected marketing business related data and identifying the processed marketing business related data, when processing the specific information input through the pre-set interface, the following steps are included: S41: Using a text classification algorithm based on a convolutional neural network to perform feature recognition and label classification on specific information input through a pre-set interface in combination with a preset risk classification method; S42: Using a classification method based on a deep learning method to judge the feature recognition results and label classification results.

7. A marketing business risk prevention and control method for spot trading scenarios according to claim 6, characterized in that: When processing the collected marketing business related data and identifying the processed marketing business related data, when processing the customer's evaluation information, the following steps are included: S51: removing some modal particles, common prepositions, punctuation marks and low-frequency words from the text information related to the evaluation information, verifying and cleaning the data, and removing noise data and interfering text from the data; S52: segmenting the text information after removing the invalid data using a neural network word segmentation method, segmenting the sentences in the text information into words one by one, and obtaining an evaluation vocabulary group containing all evaluation information; S53: removing irrelevant words in the evaluation vocabulary group, wherein the irrelevant words include dates, quantifiers, and verbs; S54: All synonyms and antonyms in the evaluation vocabulary group are merged and sorted according to the frequency of occurrence to obtain an evaluation keyword group with frequency information.

8. A marketing business risk prevention and control method for spot trading scenarios according to claim 7, characterized in that: When analyzing the external risks of marketing business based on the identification results of relevant data of marketing business, the following steps are included: S61: extracting the information in the table obtained in step S33, and extracting market change information such as commodity transaction volume change information and commodity price change information; S62: Based on market change information such as commodity transaction volume change information and commodity price change information, a risk category determination is performed using a preset risk classification method; S63: Send the determined risk category, the extracted commodity transaction volume change information, commodity price change information and other market change information to the auditor for review.

9. A marketing business risk prevention and control method for spot trading scenarios according to claim 8, characterized in that: When analyzing the external risks of marketing business based on the identification results of relevant data of marketing business, the following steps are also included: S71: extracting the information in the table obtained in step S33, and extracting the recognition result of the specific information input through the preset interface; S72: According to a preset risk classification method, a risk classification is determined for the identification result of the specific information input through a preset interface; S73: According to pre-set rules, the risk levels obtained in steps S62 and S72 are combined to obtain a final risk category determination result.

10. A marketing business risk prevention and control method for spot trading scenarios according to claim 9, characterized in that: When analyzing the internal risks of marketing business based on the identification results of relevant data of marketing business, the following steps are included: S81: searching in a pre-set risk question library according to the evaluation keyword group with frequency information to obtain the risk question corresponding to each evaluation keyword; S82: Score each evaluation keyword group according to the frequency information and the corresponding risk issues; S83: using a weighted average method to synthesize the scores of all evaluation keyword groups; S84: Based on the set rules, different risk management methods are called up according to different scores.

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