A new trigonometric kernel function has been developed to improve the accuracy of support vector machine (SVM) classification. This function includes a trigonometric term and is different from existing kernel functions. Tests on various datasets show that SVM using the new trigonometric kernel and a mix of kernels achieve the highest classification accuracy compared to traditional methods like Gaussian and polynomial kernels. The new kernel function also performs well in support vector regression (SVR) tasks.