Online public opinion events occur frequently. The effective governance of online public opinion is in an increasingly important position. This study creatively constructs an Opinion Lexicon to categorize and assign scores to words in major news commentaries. The model utilized in the article is based on KNN algorithm and BP neural network. The usefulness of the model is to screen and score samples of news comments from three representative media platforms, namely “People’s Daily Online”, “IFENG.com” and “Paper”. Through data processing and result comparison of the samples, the study can correctly predict the value of news opinion guidance and the scores assigned to comments. On the one hand, the study wants to provide an effective way for public opinion governance of Internet News. On the other hand, it promotes other news media to think about the key and initiatives of online opinion guidance from the perspective of social opinion orientation.

This study extends the application field of VR technology, and studies on the physical image problem of inventory management in ERP immersive experiment under VR technology, using the SSD block matching algorithm and the panoramic image in-depth information extraction algorithm to extract the two-dimensional in-depth information in the panoramic images. Then we reconstructed the object in 3D. The results show that the image reconstructed in 3D is clearer, more stereoscopic, and interactive, which solve the problems of low resolution, distortion, and low interaction in ERP virtual teaching environment, while using multimedia and camera roaming to enhance students’ immersive experience, achieve high-precision, real scene, better virtual teaching environment interaction. It can be seen that the immersive ERP experiment based on VR technology is conducive to improving students ’professional ability and docking ability, as well as improving students’ overall cognitive ability and operation ability of the ERP system.

This study applied VR technology to the ERP simulation experiment teaching, introducing the optimized collision detection algorithm. In the established virtual ERP simulation experiment environment, the data visualization operation is carried out on the very important data content in the ERP experiment, so that the system can predict the students’ operation in real time when the students interact. Thus, it reduces the unreality of the interaction. In addition, we improve the AABB bounding box based on B+ tree storage to improve the efficiency of inter-model collision detection and reduce the occupation and consumption of system memory during detection. Finally, the framework of Bayesian predictive filtering algorithm is introduced to predict the students’ simple interactive operation and the orientation of object model. This model optimizes the whole immersive experience teaching of ERP based on VR technology, and makes a certain contribution to meet the talent demand in the context of big data.

This study applied support vector machine algorithm and adaptive-boost algorithm to analyze the best division hyperplane of enterprise resource planning experimental teaching. We used two groups of experimental data to apply support vector machine and adaptive-boost algorithm. To complete data preprocessing and assign different weights of each index we applied adaptive-boost algorithm. Then we used the SVM to calculate and classify the expected samples. After two sets of experiments, the results show that the expected samples classified by support vector machine and adaptive-boost algorithm have a better fit with the actual experimental situation. It means that the algorithm improves the ability of digital intelligent prediction and feedback in experimental teaching. It supplies a reference for the experimental teaching of the immersive economy and management major in the future.