News classification is important for people to organize web information. There are many connections between daily news, for example, they may be different reports about the same event or the same person. However, previous works classify news mainly based on single news, they ignore relationships between multiple news. This paper innovatively utilizes relationships between multiple news, such as their relevance in time, place and people, to classify news. To take full advantage of these relationships and integrate various information of multiple news, we propose News Classification Graph (NCG), a heterogeneous graph with different types of nodes and edges. Furthermore, we propose Joint Heterogeneous graph Network (JHN) to properly embed the NCG. It fully utilizes the information of heterogeneous nodes and heterogeneous edges in NCG. Extensive experiments carried on four news datasets demonstrate the effectiveness of our work in solving the news classification problem.