Abstract: With the advancement of gas sensing technology, fire early-warning instruments integrating multiple gas sensors exhibit significant application potential in the early detection of combustion, offering a promising approach for fire prevention and rapid intervention. However, multi-sensor systems generate large volumes of heterogeneous data, posing challenges for data processing and fusion. Addressing the issues of delayed abnormal feature capture and the difficulty of timely classification of combustion scenarios in the early stages of fire incidents, this study proposes an early-warning and classification strategy that integrates a One-Class Support Vector Machine (OCSVM) with a Few-Shot Learning (FSL) framework. This strategy is designed for early combustion warning and scenario recognition. By incorporating a sliding-window data processing method with the OCSVM model, the proposed approach enables early anomaly detection and real-time alarm triggering. Furthermore, leveraging the Meta-Transformer model within a meta-learning framework, the method achieves precise classification of four combustion scenarios. A prototype network module is introduced to enhance the model’s ability to recognize unseen scenarios under few-shot learning conditions. Experimental results demonstrate that the proposed method effectively captures abnormal features within the first five minutes of combustion, achieving an early-warning accuracy of 97.73%. Following the warning phase, the recognition accuracy for seen and unseen combustion scenarios reaches 97.33% and 94.5%, respectively. Additionally, in a rapid-response validation, the average recognition accuracy for seen and unseen combustion scenarios within the first ten minutes reaches 94.21% and 95.42%, respectively. This study provides a novel approach for the development of multi-sensor fire early-warning instruments tailored for early combustion detection and recognition. Furthermore, it holds potential for supporting the advancement of intelligent fire emergency management systems.