Knowledge graphs are involved in more and more applications to further improve intelligence. Owing to the inherent incompleteness of knowledge graphs resulted from data updating and missing, a number of knowledge graph completion models are proposed in succession. To obtain better performance, many methods are of high complexity, making it time-consuming for training and inference. This paper proposes a simple but effective model using only shallow neural networks, which combines enhanced feature interaction and multi-subspace information integration. In the enhanced feature interaction module, entity and relation embeddings are almost peer-to-peer interacted via multi-channel 2D convolution. In the multi-subspace information integration module, entity and relation embeddings are projected to multiple subspaces to extract multi-view information to further boost performance. Extensive experiments on widely used datasets show that the proposed model outperforms a series of strong baselines. And ablation studies demonstrate the effectiveness of each submodule in the model.