Abstract: In view of problems of existing health state evaluation methods for shearer, such as low assessment accuracy due to the great influence of human factors on determination of evaluation index weight, weak local search ability and poor anti-interference ability and insufficient ability to find the global optimal value of the single evaluation algorithm, an intelligent evaluation method of health state of shearer based on principal component analysis(PCA) and BP neural network optimized by genetic algorithm（GA）algorithm (PCA-GA-BP algorithm) was proposed. Firstly，according to structure and working principle of shearer, the state monitoring points of the shearer are selected to obtain various state parameters of the shearer's health state. PCA is used to reduce data dimensions and extract the data characteristics of the shearer's state parameters to avoid complication of BP neural network input. Then，GA is introduced to find the global optimal weight for the traditional BP neural network. Finally, an intelligent evaluation model of shearer's health state based on GA-BP is established by training parameters, and the state parameters of the shearer are automatically input into the evaluation model. The test results is output through intelligent evaluation algorithm, self-learning, self-optimization and self-judgment of shearer's health state are realized. The experimental results show that the intelligent evaluation method of health state of shearer based on PCA-GA-BP algorithm can accurately, rapidly and intelligently evaluate the health state of shearer. Compared with evaluation method based on single BP algorithm, it has shorter training time, simpler evaluation process and higher evaluation accuracy, up to 97.08%.