Graph convolutional neural networks obtain the node representation by aggregating the neighbor node information with high similarity，and selecting the appropriate neighborhood for the node and conducting effective aggregation are the keys to the graph convolutional networks. Most of the existing graph convolutional neural networks directly aggregate the node information in the multi-hop neighborhood，without considering the difference of the aggregation weights of different hop neighborhoods on different nodes in the network. Aiming at this，a path connectivity based neighbor-awareness node classification algorithm （PCNA） is proposed. The node neighborhood is determined by the path connectivity information in the network，and the influence weight of different length paths on the similarity calculation between nodes is adaptively perceived to guide the neighborhood aggregation process of graph convolutional neural network. Specifically，PCNA is composed of a neighborhood perceptron and a node classifier. The neighborhood perceptron adaptively obtains the aggregated neighborhood of each node and the influence weights of paths with different lengths based on the reinforcement learning mechanism，and then uses the path connectivity information between nodes to obtain the similarity matrix. The node classifier uses the obtained similarity matrix to perform neighborhood aggregation to obtain node representation and classify nodes. The comparison experiments with 10 classical algorithms on eight real datasets show that the proposed algorithm has better performance in node classification tasks.