The invention discloses a lung cancer image fine classification method based on fusion of LBP and wavelet moment features. The lung cancer image fine classification method is characterized in that S1, nidus positioning of an input image can be carried out; S2, a plurality of templates of the nidus part can be generated randomly; S3, the scaling of the input image in different dimensions can be carried out, and the extraction of the textural feature MB-LBP and shape feature wavelet moment of the image block and the template block can be respectively carried out, and then the two features can be integrated by adjusting the weighting parameters by the experiment; S4, the feature response image can be acquired by matching the different positions of the image; S5, the response image can be converted into the feature vector by adopting the improved mean space pyramid model; S6, the fine classification can be realized by adopting the vector machine. The algorithm provided by the invention is the attempt of the fine classification idea in the medical science field, and the generating of the redundant templates can be reduced. The lung cancer image information can be represented by the well fusion of the LBP textural features and the wavelet moment features. By adopting the pyramid model extraction model, the strong features can be maintained, and the identification precision can be improved.