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A novel method for wild fungi classification based on YOLOv8 is proposed to address the challenges posed by the diverse range of wild fungi species and the low accuracy and efficiency of traditional manual identification methods. The approach introduces the C2f-MSBlock convolution to enhance the extraction of multi-scale features from wild fungi while reducing computational costs. Additionally, a multi-head self-attention mechanism is incorporated to prevent the model from converging to local optima, thereby improving classification accuracy. To better capture fine-grained details such as fimbriae and spots, an asymmetric dual detection head scheme is proposed, overcoming the limitations of traditional detection heads. To address the issue of loss in bounding box regression, the Inner-CIOU loss function is employed, enabling flexible adjustment of bounding box scales and reducing regression loss. Experimental results demonstrate that, compared to the best 3DRe-YOLO algorithm in the field of wild fungi, the proposed method achieves a 0.3% improvement in mAP@0.5, a 1.2% increase in accuracy, and a 0.3% improvement in recall, validating the effectiveness of the proposed enhancements.