This paper proposes an internet tourism scene classification algorithm, named multi-feature fusion with transfer learning, which utilizes unlabeled auxiliary data to facilitate image classification. Firstly, we do the SURF extraction and MRHM analysis for the training data separately, in which the training data set as combined with labeled images and unlabeled auxiliary images. Then we compute the target feature vector for each image by merging the extended SURF descriptor and MRHM feature. Finally, we train the SVM classifier scene classification. Due to the capability of transferring knowledge, the proposed algorithm can effectively address insufficient training data problem for image classification. Experiments are conducted on a Beijing tourism scene dataset to evaluate the performance of our proposed algorithm. The experimental results are encouraging and promising.