Semi-supervised Gaussian mixture model(SGMM)based on labeling nodes can improve the accuracy of model parameter estimation. However,the accuracy and convergence of the Expectation Maximization(EM)algorithm are affected by the amount of overlap and mixing coefficients among the Gaussian distributions. In order to improve the accu-racy and speed of the SGMM parameter estimation,the Anti-annealing is combined with the EM algorithm of SGMM. A clustering algorithm of the semi-supervised Gaussian mixture model based on anti-annealing(ASGMM-EM) is proposed. The inverse temperature parameter of the algorithm increases from a smaller value to an upper bound that more than 1 and then back to 1. The semi-supervised clustering EM algorithm is implemented at each inverse temperature parameter. Experiments on synthetic and real data show that the ASGMM-EM is better compared to the algorithms only using semi-supervised or anti-annealing technique.