In view of the real-time, sparse and massive characteristics of micro-blog, a topic discovery model based on real-time co-occurrence network was proposed. Firstly, the set of keywords was extracted from the primitive data by the model, and the relationship weights was calculated on the basis of the time parameter to structure the word co-occurrence network. Then, sparsity could be reduced by finding potential features of a strong correlation based on weight adjustment coefficient. Secondly, the topic incremental clustering could be achieved by using the improved Single-Pass algorithm. Finally, the feature words of each topic were sorted by heat calculation, so the most representative keywords of the topic were got. The experimental results show that the accuracy and comprehensive index of the proposed model increase 6%, 8% respectively compared with the Single-Pass algorithm. The experimental results prove the validity and accuracy of the proposed model.