Simulation of the Training Path of Film Creation Talents Based on Artificial Neural Network

Abstract

Abstract In today's changing social background, film creative talents cultivated through higher education are difficult to fully adapt to the environment. This paper tries to break the bottleneck of film creation talents by introducing artificial neural network technology, and is committed to cultivating innovative talents. By using artificial neural network technology, the optimal model can be obtained based on target training samples. The main unit of this network technology is neuron. Through the design experiment, we can know that if we want to obtain the best convolutional network reconstruction model, the probability p value must be set to 0.1. Under this environment, the accuracy and convergence speed of the model will be greatly improved, so as to effectively analyze the research on creative talent training path. Then, this paper analyzes the feasibility and necessity of film creation talent training by establishing a talent development prediction function. Through the innovation ability test of the target group and the comprehensive description of the function realization, the following characteristics are obtained: the target student group lacks innovation awareness, but has strong skills and rich imagination. Although the innovation personality is outstanding, the overall innovation ability is still low. Therefore, it is necessary to improve the innovation level of the target student group. Based on this, this paper puts forward several suggestions for talent cultivation. This paper designs several training paths by introducing artificial neural network into the training process of film creation talents.