Multi-sensor fire detection technology has been widely adopted in practical applications; however, existing methods still suffer from high false alarm rates and inadequate adaptability in complex environments due to their limited capacity to capture deep time-series dependencies in sensor data. To enhance the robustness and accuracy of fire detection, this paper proposes a fire detection model based on a Bidirectional Long Short-Term Memory network with Layer Normalization and Self-Attention (BiLSTM-LN-SA). The model employs a Bidirectional LSTM (BiLSTM) to autonomously extract intricate time-series features and long-term dependencies from multi-sensor data. Furthermore, Layer Normalization(LN) is introduced to effectively mitigate feature distribution shifts across different environments, thereby improving the model's adaptability to cross-scenario data distributions and generalization capability. Coupled with a self-attention mechanism that dynamically evaluates the importance of features at different time steps, the model adaptively enhances fire-critical information and achieves deeper dynamic process-aware feature fusion. Experimental results on a real-world fire dataset demonstrate that the BiLSTM-LN-SA model effectively identifies fire events, exhibiting superior detection performance.