A Predictive business Process Monitoring （PPM） method based on concept drift was proposed to solve the problems of model accuracy decreasing over time and poor real-time performance in existing Business Process Monitoring （BPM） methods. Firstly， the event log data was preprocessed and encoded. Secondly， a Bidirectional Long Short-Term Memory （Bi-LSTM） network model was used to capture enough sequence information from both forward and backward directions in order to build the business process model. At the same time， the attention mechanism was utilized to fully consider the contributions of different events to the model’s prediction results， and assign different weights to event logs to reduce the influence of noise on the prediction results. Finally， the executing instances were input into the constructed model to obtain the predicted execution results， and the results were used as historical data to fine-tune the model. By testing on 8 publicly available and real datasets， the results show that the proposed method has an average prediction accuracy improvement of 5.4%-23.8% compared to existing BPM methods such as Support Vector Machine （SVM）， Logistic Regression （LR）， and Random Forest （RF）， and the proposed method also outperforms existing research methods in terms of earliness and timeliness.