To improve the analysis effect of e-sports competitions, this paper studies the intelligent analysis methods of e-sports, compares the advantages and disadvantages of various multi-classification methods, and innovatively designs a two-layer SVM classifier structure for four types of motor imagery EEG signals. Moreover, this paper uses the competition data set to test the classification accuracy of the designed double-layer SVM classifier structure and compares it with the DAG-SVM multi-classification method. The research results show that the e-sports competition analysis system based on the intelligent analysis system proposed in this paper has a good e-sports competition analysis effect, and has a good effect in e-sports competition prediction.

In order to improve the effect of e-sports training, this paper combines the intelligent gesture recognition technology to construct an e-sports training system and judges the training effect of players through the recognition of players’ gestures. Moreover, this paper studies the commonly used feature extraction algorithms and proposes an improved SLC-Harris feature extraction algorithm, and the feasibility of this algorithm is verified by the experimental results on the EUROC data set. In addition, this paper uses the KLT optical flow algorithm to track the extracted feature points and calculates the pure visual pose through epipolar geometry, triangulation, and PnP algorithms. The experimental research results show that the electronic economic training system based on intelligent gesture recognition proposed in this paper has certain effects.