In this paper, we propose a channel-split-based dual-branch block (CDBlock) based on the PointMLP method, which splits input features into two groups along the channel dimension and feeds them into different branches of the dual-branch network module. Specifically, our proposed CDBlock consists of a lightweight branch and a heavyweight branch. The lightweight branch, which uses the structure of the residual MLP, is designed to extract coarse semantic features. The heavyweight branch, which uses the bottleneck network as backbone, is responsible for extracting more deeper and distinguishable information. By doing this, our proposed CDBlock effectively improves the network's feature extraction and learning capabilities. Experimental results show that our proposed method outperforms the existing PointMLP by achieving an overall accuracy of 86.2% and a class average accuracy of 84.97% on ScanObjectNN dataset. In particular, our approach achieves better results with using fewer parameters and computational cost than the PointMLP. Additionally, our approach also achieves encouraging performance on the ShapeNetPart dataset.