Early mycosis fungoides （MFs） may present as erythematous scaly skin lesions， which are difficult to distinguish from benign inflammatory skin diseases such as psoriasis and chronic eczema. This paper presents a new method based on multimodal image fusion for early mycosis fungoides recognition. The method adopts the ResNet18 network to extract features of single-modality images based on dermoscopic images and clinical images， designs the cross-modal attention module to achieve feature fusion of two modal images， and uses the self-attention module to extract the key information and reduce redundant information in the fusion features， thereby improving the accuracy of intelligent identification of early mycosis fungoides. Experimental results show that the proposed intelligent diagnosis model outperforms the comparison algorithms. At the same time， the proposed intelligent model is applied to the actual clinical diagnosis of dermatologists. Through the changes in the average diagnostic accuracy of the experimental group and the control group， it is confirmed that the proposed intelligent diagnostic model can effectively improve the clinical diagnosis level.