Although diverse solutions for link prediction have been provided by Graph Neural Networks （GNN）， the recent models have significant shortcomings in fully utilizing high-order and asymmetric information between vertices caused by the structural constraints of ordinary graphs. To address the above issues， a link prediction model based on directed hypergraph adaptive convolution was proposed. Firstly， the directed hypergraph structure was employed to represent high-order and directional information between vertices more sufficiently， possessing advantages of both hypergraphs and directed graphs. Secondly， an adaptive information propagation method was adopted by directed hypergraph adaptive convolution to replace the directional information propagation method in traditional directed hypergraphs， thereby solving the problem of ineffective updating of embeddings for tail vertices of directed hyperedges， and solving the problem of excessive smoothing of vertices caused by multi-layer convolution. Experimental results based on explicit vertex features on Citeseer dataset show that the proposed model achieves a 2.23 percentage points increase in the Area Under the ROC （Receiver Operating Characteristic） Curve （AUC） and a 1.31 percentage points increase in Average Precision （AP） compared to the Directed Hypergraph Neural Network （DHNN） model in link prediction task. Therefore， it can be concluded that this model expresses the relationships between vertices adequately and improves the accuracy of link prediction task effectively.

Due to objective factors such as small inherent features and the depth of the network causing feature loss， the detection of small objects is always a challenging issue in the field of object detection. To address the above issues， a model for optimizing the detection of small objects was proposed based on multiple feature enhancements based on the network structure. Firstly， the optimization of gradient calculation was achieved by replacing Spatial Pyramid Pooling （SPP） in the backbone network. Secondly， a multi-level bidirectional fusion at the feature level and the addition of Adaptive Feature Fusion （AFF） module to the output head were employed in the network neck to achieve multi-level feature enhancement. Experimental results show that on COCO2017-val dataset， when the IoU （Intersection over Union） is 0.5， the average precision of the proposed model reaches 61.4%， which is 4.7 percentage points higher than that of the currently popular YOLOv7 model. At the same time， the detection frame rate of the proposed model with a single GPU is 78.2 frame/s， which is in line with industrial level detection speed.