Abstract: The one-dimensional transient electromagnetic (TEM) method is time-consuming and suffers other drawbacks such as difficult parameter adjustment and heavy dependence on the initial model. Therefore, this study proposed a real-time TEM inversion method—the Attention mechanism-based convolutional neural network (CNN) - bidirectional Long Short-Term Memory (BiLSTM) (AC-BiLSTM). By fully utilizing the time difference, the AC-BiLSTM performed model training in non-observation time and the real-time inversion of the collected data in observation time. With the measured data mixed with a certain proportion of data obtained from forward modeling as the dataset, the sampling time and apparent resistivity as the input features in the form of supervised learning, and the log-constrained Occam inversion results as the learning target, the whole process of the AC-BiLSTM method is as follows: (1) the encoder-decoder model is established based on CNN and LSTM; (2) based on the data characteristics, the Attention mechanism is added to the decoder to extract the output data from the hidden layer; (3) finally, the depth-resistivity data are obtained from the fully connected layer. The study results indicate that the AC-BiLSTM algorithm can fully dig out the spatio-temporal characteristics of data and quickly yield resistivity images that meet the electrical characteristics of strata. The predicted values of the AC-BiLSTM algorithm on the TEM dataset obtained from forward modeling showed a goodness of fit of 0.898 with the forward model, with root mean squared error of 18.44 and an average relative error of 0.065. Furthermore, compared to the single LSTM neural network and the Occam method, the AC-BiLSTM algorithm showed that the goodness of fit was improved by 0.086 and 0.176, respectively, the root mean squared error was reduced by 2.97 and 9.32, and the average relative error was reduced by 0.012 and 0.068, respectively. The AC-BiLSTM inversion of measured TEM data from the V8 Receiver enabled the quick and accurate stratification of strata in the study area and the delineation of the distribution range of coal mine goaf, with the obtained results consistent with the actual situation. Research results break through the limitations of traditional inversion methods and improve the accuracy and defficiency of transient electromagnetic data interpretion.