Aiming at the existing problems in user trajectory data modeling such as the sparsity of check-in points, long-term dependencies and complex moving patterns, a social network user trajectory prediction model based on self-supervised learning, called SeNext, was proposed to model and train the user trajectory to predict the next Point Of Interest (POI) of the user. First, data augmentation was utilized to expand the training trajectory samples, which solved the problem of the deficiency of model generalization capability caused by insufficient data and too few footprints of some users. Second, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and attention mechanism were adopted into the modeling of current and historical trajectories respectively, so as to extract effective representations from high-dimensional sparse data to match the most similar moving patterns of users in the past. Finally, SeNext learned the implicit representations in the latent space by combining self-supervised learning and introducing contrastive loss Noise Contrastive Estimation (InfoNCE) to predict the next POI of the user. Experimental results show that compared to the state-of-the-artVariational Attention based Next (VANext)model, SeNext improves the prediction accuracy about 11% on Top@1.