In order to improve the representation ability of multi-kernel learning and reduce its computational cost, this paper proposes a least squares twin support vector machine(LSTWSVM) algorithm that combines depth feature and multi-kernel learning. Aiming at the problem of high computational complexity of kernel classifiers such as support vector machine in multi-kernel learning, a multi-kernel LSTWSVM framework based on the principle of edge error minimization is proposed. The cost-sensitive learning idea is adopted to improve the performance of multi-kernel learning by using the advantages of classifiers. Aiming at the problem of Gauss multi-kernel shallow structure, a highly robust depth mapping kernel based on depth neural network multi-layer information is designed using the MKL method. The depth kernel and multi-scale Gaussian basis kernel are fused in the form of kernel matrix Hadamard product to construct a new set of improved cores with high expressiveness, which contains the deep feature information of data. Finally, this paper highly integrates the multi-kernel training algorithm based on the LSTWSVM with the improved multi-kernel structure. Through benchmark datasets and industrial experiments, it shows that it can combine the advantages of deep learning and multi-kernel learning, and improve the classification accuracy and generalization ability at a lower computational cost.