According to the richness of user and item information in Knowledge Graph （KG）， the existing recommendation methods with graph embedding propagation can be summarized into three categories： user embedding propagation， item embedding propagation， and hybrid embedding propagation. The user embedding propagation method focuses on using items interacted with users and KG to learn user representations； the item embedding propagation method uses entities in KG to represent items； the hybrid embedding propagation method integrates user-item interaction information and KG， addressing the issue of insufficient information utilization in the first two methods. The technical characteristics of these three methods were deeply compared by specifically analyzing the key technologies of the three core tasks in the recommendation methods with graph embedding propagation： graph construction， embedding propagation， and prediction. At the same time， by replicating mainstream models in each category of methods on general datasets such as MovieLens， Booking-Crossing， and Last.FM， and comparing their effects using the CTR （Click-Through Rate） metric， it is found that the recommendation method with hybrid embedding propagation has the best recommendation performance. It combines the advantages of user and item embedding propagation methods， utilizing interaction information and KG to enhance the representations of both users and items. Additionally， a comparative analysis of various categories of methods was performed， their advantages and disadvantages were elaborated， and the future research work was also proposed.

To solve the problem of low detection precision of weakly perceived objects with missing shapes in distant or occluded scenes， a Weakly Perceived object detection method based on point cloud Completion and Multi-resolution Transformer （WP-CMT） was proposed. Firstly， since that some key information was lost due to the down-sampling convolution operation in object detection network， the Part-Aware and Aggregation （Part-A²） method with deconvolution up-sampling structure was chosen as the basic network to generate the initial proposals. Then， in order to enhance the shape and position features of the weakly perceived objects in the initial proposals， the point cloud completion module was applied to reconstruct the dense point sets on the surface of the weakly perceptive objects， and a novel multi-resolution Transformer feature encoding module was constructed to aggregate the completed shape features with original spatial location information of the weakly perceived objects， and then the enhanced local features of the weakly perceived objects were captured by encoding the contextual semantic correlation of the aggregated features on local coordinate point sets with different resolutions. Finally， the refined bounding boxes were generated. Experimental results show that WP-CMT achieves 2.51 percentage points gain on average precision and 1.59 percentage points on mean average precision compared to baseline method Part-A² for the weakly perceived objects at hard level in KITTI and Waymo datasets， which proves the effectiveness of the proposed method for weakly perceived object detection. Meanwhile， ablation experimental results show that the point cloud completion and multi-resolution Transformer feature encoding modules in WP-CMT can effectively improve the detection performance of weakly perceived objects for different Region Proposal Network （RPN） structures.

Poor generalization of feature parameters classification and large category computation reduce the classification performace of K-Nearest Neighbor (KNN). An improved KNN based on union entropy under the attribute reduction condition was proposed. Firstly, the size of classification impact of data feature was measured by calculating the union entropy of two feature parameters relative to any two condition attributes, and the intrinsic relation was established between classified features and the specific classification process. Then, the method which reduced condition attributes according feature union entropy set was given. The theoretical analysis and the simulation experiment show that compared with the classical KNN, the improved algorithm has better classification performance.