In the non-rigid point set registration algorithm, whether the correct correspondence can be found plays an important role. Generally the corresponding points in two point sets have similar neighborhood structures besides the close distance. Therefore, a non-rigid point set registration algorithm based on global and local similarity measurement was proposed. Firstly, the Coherent Point Drift (CPD) algorithm was used as the registration framework, and the Gaussian mixture model was used to model the point sets. Secondly, the global and local mixture distance was improved to form the global and local similarity measurement criterion. Finally, the correspondence and the transformation formula were solved by the Expectation Maximization (EM) algorithm. In the initial stage of the iteration, the proportion of local similarity was larger so that the correct correspondence was able to be found rapidly; with the progress of the iteration, the proportion of global similarity was increased to ensure the smaller registration error. Experimental results show that compared with the Thin Plate Spline Robust Point Matching (TPS-RPM) algorithm, the Gaussian Mixture Models point set REGistration (GMMREG) algorithm, the Robust Point Matching algorithm based on L ₂E estimation (RPM-L2E), the Global and Local Mixture Distance and Thin Plate Spline based point set registration algorithm (GLMDTPS) and the CPD algorithm, the proposed algorithm has the Root Mean Squared Error (RMSE) decreased by 39.93%, 42.45%, 32.51%, 22.36% and 11.76% respectively, indicating the proposed algorithm has better registration performance.