Aiming at the high-precision classification problem of digital images, a new digital image classification algorithm is proposed. In this algorithm, the local binary patterns (LBP) operator is used to construct the LBP image of the digital image, and then the histogram of the LBP graph is used to construct the feature vector of the image sample. Finally, the training data set constructed by the eigenvectors of a large number of samples is sent to the least squares support vector machines (LSSVM) for the construction of the classification model. In the classification test of test data sets, the proposed algorithm is compared with traditional support vector machine algorithm, extreme learning machine algorithm and Hopfield neural network method. The LBP-LSSVM algorithm presented shows excellent performance in the testing of typical performance indicators such as macro-precision rate, macro-recall rate and classification time.