A Method of Applying Virtual Reality Converged Remote Platform Based on Crawfish Optimization Algorithm to Improve ESN Network

INTRODCTION: Immersive teaching and learning methods based on virtual reality-integrated remote platforms not only allow foreign language learners to learn in a vivid and intuitive learning environment, but also provide good conditions for multi-channel perceptual experiences of foreign language learners in terms of sight, sound and touch. OBJECTIVES: To address the problems of insufficiently systematic analysis and quantification, poor robustness and low accuracy of analysis methods in current effect analysis methods. METHODS: This paper proposes an effect analysis method of virtual reality fusion remote platform based on crawfish optimization algorithm to improve echo state network. First, the effect analysis system is constructed by analyzing the process of virtual reality fusion remote platform and extracting the effect analysis influencing elements; then, the echo state network is improved by the crayfish optimization algorithm and the effect analysis model is constructed; finally, the high accuracy of the proposed method is verified by the analysis of simulation experiments. RESLUTS: The proposed method improves the accuracy of the virtual reality fusion remote platform effect analysis model, the analysis time is 0.002s, which meets the real-time requirements, and the number of optimization convergence iterations is 16, which is better than other algorithms. CONCLUSION: The problems of insufficiently systematic analytical quantification of effect analysis methods, poor robustness of analytical methods, and low accuracy have been solved.