In order to improve the accuracy and classification speed of fault diagnosis of valve cooling equipment in converter station, a fusion feature algorithm based on Fisher ratio criterion and a fault classification model based on particle swarm optimization least squares support vector machine are proposed. Firstly, the static parameters and dynamic first-order difference parameters of Mel cepstrum coefficient and inverse Mel cepstrum coefficient are extracted as fault feature quantities respectively, and all the high and low frequency information of valve cooling equipment fault is obtained. Then, Fisher ratio criterion is used to fuse the fault features of valve cooling equipment twice, so as to reduce the repeated data and interference signal caused by direct superposition signal. The 1×13 dimensional Fisher ratio data is selected as the fusion feature of the noise signal of the valve cooling equipment. Secondly, in order to improve the accuracy and classification speed of LSSVM algorithm fault identification, the PSO algorithm is used to optimize the kernel function bandwidth and penalty factor of LSSVM algorithm, and the optimal solution of the two parameters is obtained, and the LSSVM valve cooling equipment fault classification model is established. Finally, the main pump between the valve cooling equipment is taken as an example, and different feature fusion algorithms and fault identification methods are used for comparative analysis. The results of the example verify that the proposed method can quickly and accurately identify the fault signals of the valve cooling equipment at different frequencies, and the accuracy of fault identification can reach 96.67%.