To solve the problem of high false detection rates in tunnel fire detection caused by the complexity of tunnel environments based on the YOLOv8n network model， an improved tunnel fire detection algorithm was proposed.First， in the backbone network， the FasterNet network was used for replacement while retaining the original SPPF module to achieve more comprehensive feature extraction； Secondly， in order to improve the detection accuracy of the model for irregular targets in the complex background， the D-LKA attention mechanism was introduced in the C2f module； Finally， Focaler-IoU to optimize the model loss function was introduced， which further reducing the problem of false positives or false negatives caused by distractors. The experimental results show that compared with YOLOv5， YOLOv7 and the original models of YOLOv8n， the accuracy of the improved model is increased by 7.6%， 5.6%， and 3.5% respectively， and the average accuracy means are increased by 8.3%， 7.7%， and 5.1% respectively. Compared with other YOLOv8n-based improved algorithms， the mean average precision of our proposed model is increased by 3.3% and 6.4% respectively.