In the Chinese fund market, performance benchmarks and various investment styles make it difficult for investors to identify the merits of fund performance. Fund ratings can map market information to assist investors and regulators in making decisions, and have important reference and monitoring values. This paper used the classic evaluation index of funds to establish a fund rating index system, used the BP neural network to rate the fund, and used the genetic algorithm with excellent nonlinear optimization ability to optimize the initial weight threshold of the BP neural network, and constructed a multi-input GA- BP neural network fund rating model. Experiments showed that the BP neural network optimized by the genetic algorithm could converge faster during training, and was superior to the BP neural network in terms of simulation ability and error level, and could evaluate the fund level more accurately.