In unstructured environments, the 6-DoF object grasp is ahighly challenging task in the field of intelligent service robotics. In such scenarios, robots need to deal with interferences from objectsof different sizes and shapes,as well as environmental noise,making it difficult to generate accurate grasp poses. To address this problem, this article proposes a grasp generation method based on multi-scale features fusion and grasp quality evaluation. Firstly, an adaptive radius query method is introduced to solve the issue of key points query anomalies caused by uneven point cloud sampling in real environments. Secondly, a grasp generation network is designed to fuse multi-scale features and grasp quality assessment, which enable the generation of rich 6-DoF grasp candidates. Finally a grasp quality assessment method is defined, which includes force closure score,contact surface flatness,edge analysis,and centroid score. These criteria are applied to generate new grasp confidence score labels on a standard dataset and incorporated into the grasp generation network. Compared with the current state-of-the-art method FGC-GraspNet, the experimental results show that the described method improves the average accuracy by 5.9%, the success rate of single-object grasp by 5.8%,and the success rate of multi-object scene grasp by 1.1%.In summary,the proposed method has effectiveness and feasibility, which has good adaptability in single-object scenes and multi-object scenes.