The classification of pancreatic cystic neoplasms into benign and malignant categories is crucial for medical decision-making. This paper is dedicated to enhancing the accuracy of pancreatic cystic neoplasms classification to assist physicians in formulating more precise diagnostic and therapeutic plans. Utilizing radiomics technology and the ResNet50 neural network， a novel classification method for pancreatic cystic neoplasms is proposed， integrating multi-kernel learning and multi-source feature fusion. The key steps of this method include feature selection， kernel matrix fusion， and the construction of the classification model. Feature selection is performed using the least absolute shrinkage and selection operator （LASSO） to reduce redundant features and improve the model’s generalization ability. Subsequently， multi-source features， screened through feature selection， are mapped in basic kernel functions to construct basic kernel matrices for multi-source features. The weights of these kernel matrices are then optimized and summed up to form a fused kernel matrix. Finally， a support vector machine （SVM） classifier is utilized to categorize pancreatic serous and mucinous cystic tumors. The significance of this process lies in SVM’s ability to use the kernel matrix for inner product operations in high-dimensional spaces， thereby finding a hyperplane to classify data in such spaces. The fused kernel matrix， containing multi-source information after feature mapping， provides higher-dimensional and more complex feature representations. Experimental results demonstrate significant performance improvements in the classification task of pancreatic cystic neoplasms， offering more reliable auxiliary information to physicians and holding substantial clinical application potential.