Time series classification is the basis of streaming data event analysis and data mining. A T-CNN time series classification method based on Gram matrix was proposed, aiming at the problems of loss of time attribute, low classification accuracy and low efficiency of the existing methods. Specifically, the time series was denoised by wavelet threshold to filter out normal curve noise, and a lossless transformation method based on the Gram matrix was proposed to convert the time series into time-domain images and retain all event information. Then the CNN classification method of time series was improved, and the Toeplitz convolution kernel matrix was introduced into the convolutional layer calculation to realize the replacement of convolution operation with matrix product. The Triplet network was introduced to construct the T-CNN classification model, and the square loss function of CNN was optimized by calculating the similarities between similar events and different kinds of events, so as to improve the convergence rate of gradient descent and the classification accuracy of the T-CNN model. Experimental results show that compared with the existing methods, the proposed T-CNN time series classification method can improve the classification accuracy by 35%, the classification precision by 35% and the classification efficiency by 40%.