With the rapid development of social e-commerce today, the statistical analysis of consumers on the platform by traditional e-commerce platforms is no longer suitable for the statistical analysis of user behaviour under the current social e-commerce. However, the different income levels of consumers and the different behaviours of using social software have put forward higher requirements for the marketing and promotion methods of social e-commerce. Therefore, it is necessary for social e-commerce to accurately subdivide merchants to identify their value, provide consumers with differentiated services, and implement more effective user strategies. In this paper, the indicators in the traditional RFM model are matched with the characteristics of social e-commerce merchants, the number of friends of social e-commerce merchants is introduced, and a RCFM model suitable for social e-commerce transaction data segmentation is constructed. In this paper, the weight of each index in the RCFM model is calculated by the analytic hierarchy process. Finally, the superiority of the new model in precise segmentation is verified through the weighted optimization and comparison experiment of the RCFM model. This research enriches the related research on social e-commerce business models, provides ideas for social e-commerce merchants' value judgment, and provides a foundation for social e-commerce enterprises to construct social e-commerce merchant portraits and implement targeted services.

With the rapid development of computer technology and communication technology, computers have been applied to all areas of people's lives. In the field of education, it is a necessary function for the intellectualization of the education system to realize the automatic marking of examination papers by computer. In intelligent marking, the traditional method is used to segment the subjective question area of the test paper, which can not effectively segment different types of answer sheets and has the defects of low segmentation accuracy. The edge detection method is used to correct the subjective question area of the answer sheet, and the boundary box of the subjective question part is accurately screened by positioning the top, left, and bottom positioning lines of the answer sheet to complete the segmentation of the subjective question area in marking. The experimental results show that the correct rate of the new method is more than 95%, which is much higher than that of the traditional method. The new method can be widely used in intelligent marking work, and can effectively improve the efficiency of intelligent marking.

The traditional medical voice question and answer interactive system can not meet the core needs of patient consultation service, and the patient's satisfaction with the system answer is low, so the research of medical voice question and answer interactive system based on speech recognition technology is proposed. In the hardware of the system, the control core of the system is the MCU module, which controls the normal operation of the whole system, realizes the conversion of the analog signal and digital signal through the audio input device, designs the serial communication device to transmit the data information to the computer, and uses the speech recognition technology to realize the software part of the system. And through the system test, the medical voice question and answer interactive system based on speech recognition technology is compared with the traditional medical question and answer interactive system. The experimental results show that the average response time of the medical voice question and answer interaction system based on speech recognition technology to the user request is far less than the traditional system, there is no number of error requests, and the performance is better than the traditional system. Therefore, it can be proved that the performance of the medical question and answer interactive system based on speech recognition technology has reached the expected standard, and can meet the basic needs of users for medical consultation services.

With the practical application of the self-marking system, it can give an objective and fair score, but it can not meet the needs of classroom teaching. It also needs to give students rapid feedback on the vocabulary, sentence, text structure, content relevance, and other dimensions presented in the test paper. The scoring method based on artificial features is the earliest self-marking scoring method, which uses experts to design some scoring features from the language quality, content quality, and text structure of the test paper. It takes the scoring task as a regression or classification task to score or rate the test paper. In this paper, based on the deep learning theory, a method for text similarity detection using a twin network is proposed. Considering the interaction between text pairs, we integrate expressivity pooling based on bidirectional GRU and measure the similarity by distance calculation formula. The experimental data show that the two-way GRU network integrated with expression pooling can obtain the interaction between text pairs so that the extracted features of the text are more comprehensive. The model has a better effect in the study of the similarity of text pairs. This method can reduce the difference in scoring results caused by different subjective consciousness of raters, make the scoring results more objective and persuasive, and improve the accuracy and efficiency of scoring.

For the data of virtual scene, there are often heterogeneous data from different databases. If the same object is not processed in different databases, it will have a great impact on the subsequent data synchronization. After the synchronization of heterogeneous information, the differences between these heterogeneous information will be eliminated, which will bring great convenience to the subsequent data synchronization. Therefore, this paper is based on the synchronization of heterogeneous information data in remote control and unified description. The utilized heterogeneous data synchronization is feature layer synchronization. Feature sets represented by each data are extracted from each database and are synchronized into feature vectors. Then the synchronization of the feature vectors is carried out. The experimental results show that the method in this paper can accurately warn the abnormal behavior between data synchronization and realize the function of remote control stop. Through the research of virtual control problem and according to the result of data synchronization, warning is carried out to remind the remote operators to control the virtual scene position and posture in real time and to reduce the occurrence of collision accidents.