The purpose of point cloud analysis is to extract effective high-dimensional feature representations from point coordinates. However, existing methods only rely solely on global or local features. In this paper, we introduce the Dual Mechanism Residual Connection Network (DMRC-Net), a new end-to-end network architecture for more comprehensive representations. First, local features are encoded using graph convolution and fine-grained attention mechanisms, whereas global features are encoded using a multi-layer perceptron. Second, adaptive residual learning is designed to integrate two encoding results effectively. Furthermore, the structural information in local space is strengthened by collecting low-level geometric correlations. With these designs, DMRC-Net is able to capture both global and local details comprehensively. Experimental results show that our method performs well in point cloud classification, part segmentation, and semantic segmentation tasks.