The invention discloses a
risk rating method for optimizing a Hopfield neural network based on a
firefly algorithm, and the method comprises the following steps: firstly, determining a performance period and a
risk level, extracting a modeling sample customer, and obtaining customer data as a modeling
index system, the customer data comprising the
risk level and credit data affecting repayment performance; preprocessing the credit data, and randomly segmenting a
training set and a
test set; constructing a Hopfield neural network topological structure according to the data features of the modeling sample, determining the parameters of the network, and initializing the weight and threshold of the Hopfield neural network; and constructing a mapping relation between the weight and the threshold of the Hopfield neural network and a
firefly algorithm, obtaining an
optimal weight and an optimal threshold through the
firefly algorithm, and training the Hopfield neural network by using the
training set. According to the method, the
optimal weight and threshold of the Hopfield neural network are determined by using the firefly
algorithm, the convergence speed of the neural network is accelerated, the accuracy of the prediction model is improved, and the requirement of real-time evaluation of Internet financial credit can be met.