A novel method for semi-supervised classification based on Markov chain was proposed. By incorporating rewards in Markov chain model, the distances and angles between vectors were combined. The transient analysis on the model by taking advantage of the absorbing states was carried out. Improved version of the method in consideration of the diffusion reduces the error propagation. Experimental results with the real world dataset show that the model can be successfully applied in semi-supervised classification.