Abstract: Since the creative talents evaluation is a basic link of decision-making in the cultural creative industry, this study establishes an evaluation indicator system for creative talents in the cultural industry, examines common evaluation methods and the back propagation (BP) neural network evaluation method, builds an evaluation model for creative talents in the cultural industry based on the BP neural network, and evaluates the evaluation indicator system of creative talents in the cultural industry by using common methods, which provide a sample set for the training and testing of the BP neural network model. Furthermore, this article adopts the unique nonlinear mapping capability, self-learning, and strong fault-tolerant abilities of the BP neural network to construct an evaluation model of creative talents in the cultural industry based on the BP neural network and carries out case analysis and verification, which show that the evaluation model based on the BP neural network is appropriate for the evaluation of cultural creative talents. Compared with the conventional evaluation methods, the BP neural network can simulate the experts to conduct a quantitative evaluation through repeated learning and training, so as to effectively avoid human error in the evaluation process. The structure and algorithm of the BP neural network are simple, and computers can simulate the evaluation process, thus reducing the manpower for calculation.

Key words: cultural industry, creative talents, evaluation study, neural network