Focusing on low prediction accuracy of the low-order Markov model and high sparsity rate of the high-order Markov model, a moving object location prediction algorithm based on Markov Model and Trajectory Similarity (MMTS) was proposed. The moving object's historical trajectory was modeled by using Markov thinking, and trajectory similarity was acted as an important factor of location prediction. With the result set predicted by Markov model as candidate set, the trajectory similarity factor was combined to get the final prediction. The experimental results show that, compared with the k-order Markov model, the predictive capability of the MMTS method is not greatly affected with the change of training sample size and the value of k, and the average accuracy is improved by more than 8% while significantly reducing the sparsity rate of k-order Markov model. So, the proposed method not only solves the problem of high sparsity rate and low prediction accuracy of the k-order Markov model, but also improves the stability of prediction.