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Aiming at the limitation of describing the semantic attributes of target classification, this paper proposes an adaptive weighted fusion feature based zero-sampling image classification algorithm. Firstly, the fusion weights are initialized randomly. Meantime, the semantic vector features and semantic attributes of the text are fused by neural network. Then, particle swarm optimization algorithm is used to optimize the weight of feature fusion. Finally, the features of weighted fusion are regarded as the transfer knowledge of the classification of zero-sampling images. The experimental results show that the classification algorithm based on adaptive weighted fusion for the zero-sampling image has an accuracy rate of 88.9% on the Animals with Attributes (AWA) data set, which illustrates the effectiveness. What's more, the proposed algorithm also improves the stability of the classification model for the zero-sampling image compared with the fusion feature.