Vehicle to Everything (V2X) communication is an effective solution to improve traffic safety and mobility. In order to solve the problems of deep learning that requires a large amount of training data and generalization in power allocation, this paper proposes a power distribution framework that based on Graph Convolutional Network (GCN) under the three criteria of maximizing the total user rate, maximizing the total user energy efficiency. The proposed framework first converts the wireless interference channel into a graph data structure, which proves the disorder of the interference channel. Secondly, according to the characteristics of different power distribution, the GCN network structure is constructed, and the corresponding loss function is proposed. Compared with the Multilayer Perceptron (MLP) network trained based on the weighted minimum mean square error (WMMSE) algorithm， the simulation data shows that the proposed scheme is better than the comparison algorithm in terms of small sample training, scalability, and generalizability.