Network Representation Learning(NRL)can learn a latent space rep-resentation of each vertex in a topology network structure to reflect linked in-formation.Recently,NRL algorithms have been applied to obtain document embedding in linked document network,such as citation websites.However,most existing document representation methods with NRL are unsupervised and they cannot combine NRL with a concrete task-specific NLP tasks.So in this paper,we propose a unified end-to-end hybrid Linked Document Classification(LDC)model which can capture semantic features and topological structure of documents to improve the performance of document classification.In addition,we investigate to use a more flexible strategy to capture structure similarity to improve the traditional rigid extraction of linked document topology structure.The experimental results suggest that our proposed model outperforms other document classification methods especially in the case of having less training sets.