Due to the present sparse multiple kernel learning（ MKL） model may lead to discard useful information

and it’s generalization ability is degenerated when produces the sparse weight of kernel function. And the MKL method which is based on gradient descent method has slow convergence speed when close to the optimal solution. Aiming at these problems

establish elastic MKL（ EMKL） model which is based on support vector machine（ SVM） and propose an EMKL method based on Newton gradient optimization（ NO-EMKL）. In this model

the elastic item is brought in and the second order Newton gradient descent method is used. The experimental results show that the algorithm not only has better classification precision

but also has faster convergence speed.