Abstract: To enhance coal mine gas safety and improve the accuracy of gas concentration prediction, it proposes a novel model, termed MHA-BiGRU, which integrates the Multi-Head Attention mechanism （MHA） with a Bidirectional Gated Recurrent Unit（BiGRU） neural network. The approach begins with the preprocessing and reconstruction of raw gas monitoring time series data using linear interpolation and sliding window techniques, providing a robust foundation for subsequent modeling. The BiGRU model, optimized using the mean square error loss function and the Adam algorithm, effectively captures contextual dependencies within sequential data. Critical parameters, including window size and BiGRU architecture, are meticulously fine-tuned for real-time prediction tasks. The incorporation of the MHA mechanism within the BiGRU network further enhances predictive performance by enabling multiple attention heads to focus on distinct segments of the hidden state sequences, thereby capturing complex temporal patterns. These features are subsequently refined by the BiGRU layers to extract deeper temporal dependencies. The MHA layers are further optimized to enhance feature representation, with the final gas concentration prediction being generated by a fully connected output layer. Empirical results indicate that the MHA-BiGRU model significantly outperforms several baseline models, including RNN, LSTM, GRU, BiGRU, MHA-RNN, MHA-LSTM, iTransformer, and linear models. The superior accuracy achieved by the MHA-BiGRU model underscores its potential to significantly improve coal mine gas concentration predictions, thereby contributing to enhance safety monitoring in mining operations.