Concerning the difficult acquisition of training data in HyperSpectral Image(HSI),a new semi-supervised classification framework for HSI was adopted,in which both limited labeled data and abundant unlabeled data were used to train deep neural networks. At the same time,the unbalanced distribution of hyperspectral samples leads to huge differences in the classification difficulty of different samples,and the original cross-entropy loss function is unable to describe this distribution feature,so the classification effect is not ideal. To address this problem,a multi-classification objective function based on focal loss was proposed in the semi-supervised classification framework. Finally,considering the influence of spatial information of HSI on classification,combined with Markov Random Field(MRF),the sample space features were used to further improve the classification effect. The proposed method was compared with various classical methods on two commonly used HSI datasets. Experimental results show that the proposed method can obtain classification results superior to other comparison methods.