Recently, point-based networks have begun to prevail because they retain more original geometric information from point clouds than other deep learning-based methods. However, we observe that: (1) the set abstraction design for local aggregation in point-based networks neglects that the points in a local region may belong to different semantic categories, and (2) most works focus on single-scale local features while ignoring the importance of multi-scale global features. To tackle the above issues, we propose two novel strategies named semantic-based local aggregation (SLA) and multi-scale global pyramid (MGP). The key idea of SLA is to augment local features based on the semantic similarity of neighboring points in the local region. Additionally, we propose a hierarchical global aggregation (HGA) module to extend local feature aggregation to global feature aggregation. Based on HGA, we introduce MGP to obtain discriminative multi-scale global features from multi-resolution point cloud scenes. Extensive experiments on two prevailing benchmarks, S3DIS and Semantic3D, demonstrate the effectiveness of our method.