Abstract: To enhance the low recognition accuracy of traditional intelligent lost circulation models, which suffer from limited samples, this study combined the long short-term memory (LSTM) network and auto-encoder (AE) to create an integrated LSTM-AE-based intelligent lost circulation recognition model. Initially, multiple LSTM-AE models with varying numbers of hidden neurons were trained using normal samples. Several models with better recognition performance were selected as base recognizers based on their reconstruction scores. Subsequently, the recognition results from these base recognizers were fused using ensemble learning. This approach addresses the tendency of a single model to produce false alarms and missed alarms due to overlearning of local sample characteristics, thereby improving the recognition accuracy of the model. The integrated LSTM-AE model was trained and tested using 6000 sets of stand pipe pressure, outlet flow, and mud pit volume data from 18 wells under normal drilling conditions in an oilfield. The results show that the proposed method achieves a recognition accuracy of 94.7%, surpassing the recognition results of other commonly used intelligent models. This approach offers a novel method for lost circulation recognition.