Users’ social media contains their past personal experiences and potential life patterns， and the study of their patterns is of great value for predicting users’ future behaviors and performing personalized recommendations for users. By collecting Weibo data， 11 types of events were defined， and a three?stage Pipeline system was proposed to detect personal events by using BERT （Bidirectional Encoder Representations from Transformers） pre?trained models in three stages respectively， including BERT+BiLSTM+Attention， BERT+FullConnect and BERT+BiLSTM+CRF. The information of whether the text contained defined events， the event types of events contained， and the elements contained in each event were extracted from the Weibo， and the specific elements are Subject （subject of the event）， Object （event element）， Time （event occurrence time）， Place （place where the event occurred） and Tense （tense of the event）， thereby exploring the change law of user’s personal event timeline to predict personal events. Comparative experiments and analysis were conducted with classification algorithms such as logistic regression， naive Bayes， random forest and decision tree on a collected real user Weibo dataset. Experimental results show that the BERT+BiLSTM+Attention， BERT+FullConnect， BERT+BiLSTM+CRF methods used in three stages achieve the highest F1?score， verifying the effectiveness of the proposed methods. Finally， the personal event timeline was visually built according to the extracted events with time information.