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WaDang recognition can provide valuable information for digital protection of cultural relics. However, WaDang recognition is challenging due to a great number of variations, such as different characters, different background pattern, and different scale and rotation. In this paper, we try to solve these problems by using hand-crafted and deep features and integrate them into multiple instance learning, in which the features of instances are divided into two parts and their similarity is computed by multiple kernel learning. The experiments on our collected WaDang images, MUSK, and COREL datasets show that the proposed algorithm is effective and its performance is compared with other state-of-art algorithms.