Early diagnosis of skin cancer is crucial for improving patient outcomes and alleviating the burden on the healthcare system. However， the process of feature extraction in skin cancer image classification often results in information loss and challenges in simultaneously identifying independent types of features in the images. MTIFNet was proposed， which was a network that integrates three-dimensional spatial attention with information fusion. Initially， the network employed a multi-scale spatial adaptive module to extract both global and local contextual information from images during training. This module enhanced the connection between blurred pixels around lesions and the relationship in pixels at different scales. Subsequently， a three-dimensional interaction feature optimization module was introduced to facilitate connections across different dimensions， enabling the exchange and integration of information. Finally， cross-entropy loss was used to measure the difference between the predicted probability distribution and the true class distribution to optimize the accuracy of the model. The experimental results based on the ISIC 2018 and ISIC 2017 datasets indicate that the network has improved accuracy， precision， recall， and specificity to 94.32%， 91.61%， 93.00%， 98.39% and 98.57%， 98.20%， 98.47%， 99.13%， respectively. Compared to currently popular classification networks such as ResNeSt， ConvNext， and Fcanet， MTIFNet demonstrates superior capabilities in feature extraction and interaction， thereby assisting healthcare professionals in making more precise diagnostic and treatment decisions.