To address the challenges of complex models, slow detection speed, and high false detection rate during fire detection, a lightweight fire detection algorithm is proposed based on cascading sparse query mechanism, called LFNet. In the study, firstly, a lightweight feature extraction module ECDNet is established to extract more fine-grained features in different levels of feature layers by embedding the lightweight attention module ECA (efficient channel attention) in YOLOv5s backbone network, which is used to solve the multi-scale of flame and smoke in fire detection. Secondly, deep feature extraction module FPN+PAN is adopted to improve multi-scale fusion of feature maps at different levels. Finally, the Cascade Sparse Query embedded lightweight cascade sparse query module is applied to improve the detection accuracy of small flames and thin smoke in early fires. Experimental results show that the comprehensive performance of the proposed method in objective indicators such as mAP and Precision is the best on SF-dataset, D-fire and FIRESENSE. Furthermore, the proposed model achieves lower parameters and higher detection accuracy, which can meet the fire detection requirements of challenge scenes.