Fuzzy least squares projection twin support vector machine (FLPTSVM) fails to address the difficulty of parameter selection and the limitation of a single kernel function. In view of this, this paper proposes a hybrid kernel function fuzzy least squares projection twin support vector machine by wolf pack algorithm (WPA-HFLPTSVM). This paper designs a novel method to construct a hybrid kernel function by combining polynomial and Gaussian kernel functions. The wolf pack algorithm is selected to use the classification accuracy as the fitness value for comprehensive optimization of the kernel parameters and penalty parameters of the hybrid kernel function in order to obtain the optimal combination of parameters to improve the classification performance. Experimental results demonstrate that the overall performance of the hybrid kernel function outperforms other kernel functions; Compared to the classical SVM algorithm, HFLPTSVM exhibits superior classification performance and generalizability; by utilizing the WPA, we are able to search for optimal parameter combinations for HFLPTSVM.