In order to solve the problem of identifying depression tendency among students on sina microblog platform, this paper proposes a depression tendency identification model. By inviting students widely to fill in the self-rating depression scale online on campus we can get the students' score. We collect students' microblog text and ask the psychology teacher to annotate the microblog artificially. In the pretreatment stage, we use the depression emotional dictionary to reassemble the depressed emotion words that are split at the segmentation stage so as to improve the recognition accuracy rate. And then we build a classifier based on the support vector machine to train the data. Through continuous learning and feedback, we get a better classification result. Finally, this paper defines the depression index and uses it to measure the degree of depression for a period of time. The experimental results indicate that the degree of depression measured by depression index is approximately consistent with the results of the scale, the accuracy of the method being 82.35%.