Currently， Graph ATtention network （GAT） assigns different weights to entities in the neighbourhood of the target entity and performs information aggregation by introducing an attention mechanism， which makes it pay more attention to the local neighbourhood of the entity and ignore the topology between entities and relations in the graph structure. Moreover， the output embedding vectors are simply spliced or averaged after the multi-head attention， resulting in the independence of attention heads， and fails to capture important semantic information of different attention heads. Aiming at the problems that GAT does not fully mine entity structural information and semantic information when it is applied to knowledge graph reasoning task， a Fusing Entity Semantic and Structural Information for knowledge graph reasoning （FESSI） model was proposed. Firstly， TransE was used to represent entities and relationships as embedding vectors in the same space. Secondly， an interactive attention mechanism was proposed to reintegrate the multi-head attention in GAT into multiple hybrid attentions， which enhanced the interaction between the attention heads to extract richer semantic information of the target entity. At the same time， the structural information of the entity was extracted by utilizing the Relational Graph Convolutional Network （R-GCN）， and the output feature vectors of GAT and R-GCN were learned through weight matrices. Finally， ConvKB was used as a decoder for scoring. Experimental results on the knowledge graph datasets Kinship， NELL-995 and FB15K-237 show that the FESSI model outperforms most comparison models， with the Mean Reciprocal Rank （MRR） index on the three datasets of 0.964， 0.565 and 0.562， respectively.

Acetabular reamer is one of the most important surgical tools in hip replacement surgery. The milling quality of acetabular reamer on acetabulum is affected by the dimension change of cutting edges. The wear of acetabular reamer can be examined by processing 3D point cloud of acetabular reamer， so a dimensional analysis algorithm for the cutting edges of acetabular reamer based on 3D point cloud processing was proposed. Frist， an algorithm with tangency plane and maximum angle criterion were introduced in the proposed algorithm to obtain the boundary point cloud of acetabular reamer based on boundary characteristics of the tooth holes. Second， the boundary point cloud was partitioned into individual tooth hole point clouds by K-means clustering algorithm， and then the point cloud of each tooth hole boundary was searched by radius nearest neighbor search algorithm to obtain the point cloud of cutting edges belonging to different tooth holes. Finally， RANSAC （RANdom SAmple Consensus） algorithm was used to fit the point cloud of acetabular reamer to a sphere， and Euclidean distance from the point cloud of cutting edges to the center of the fitted sphere was calculated to analyze cutting edge dimensions of acetabular reamer. PCL （Point Cloud Library） was used as a development framework to process the point cloud of acetabular reamer. The accuracy of hole segmentation of the point cloud of acetabular reamer is 100%， and the accuracy of spherical fitting radius of the point cloud of the acetabular reamer is 0.004 mm. Experimental results show that the proposed algorithm has a good effect on the point cloud processing of acetabular reamer， and can effectively realize the dimensional analysis of the cutting edges of acetabular reamer.