Because of the uncertainty and complexity of remote sensing image data, it is difficult for traditional unsupervised algorithms to create an accurate classification model for them. Pattern recognition methods based on fuzzy set theory can express the fuzziness of data effectively. In these methods, type-2 fuzzy set can better describe inter-class hybrid uncertainty. Furthermore, semi-supervised method can use prior knowledge to deal with the generalization problem of algorithm to data. Therefore, a remote sensing image classification method based on Semi-Supervised Adaptive Interval Type-2 Fuzzy C-Means (SS-AIT2FCM) was proposed. Firstly, by integrating the semi-supervised and evolution theory, a novel fuzzy weight index selection method was proposed to improve the robustness and generalization of the adaptive interval type-2 fuzzy C-means clustering algorithm. The proposed algorithm was more suitable for the classification of remote sensing data with severe spectral aliasing, large coverage areas and abundant features. In addition, by performing soft constrained supervision on small number of labeled samples, the iterative process of the algorithm was optimized and guided, and the greatest expression of the data was obtained. In the experiments, SPOT5 multi-spectral remote sensing image data of the Summer Palace in Beijing and Landsat TM multi-spectral remote sensing image data of the Hengqin Island in Guangdong were used to compare the results of the existing fuzzy classification algorithms and SS-AIT2FCM. The experimental results show that the proposed method obtains more accurate classification and clearer boundaries of classes, and has good data generalization ability.