Aiming at problems of ignoring entity mention differences and lack of complexity calculation paradigm for entity-pair relation extraction in the existing entity representation learning for Document-level Relation Extraction （DocRE） tasks， a DocRE model based on Entity Representation Enhancement （DREERE） was proposed. Firstly， an attention mechanism was used to evaluate the differences of entity mentions in determining different entity-pair relations， so as to obtain more flexible entity representations. Secondly， the entity-pair sentence importance distribution computed by the encoder was used to evaluate the complexity of entity-pair relation extraction， and the two-hop information among entity-pairs was used selectively to enhance entity-pair representations. Experiments were carried out on the popular datasets DocRED， Re-DocRED and DWIE. The results show that DREERE model improves the F1 value by 0.06， 0.14， and 0.23 percentage points， respectively， and the ign-F1 （F1 score calculated by ignoring the triples that appear in the training set） value by 0.07， 0.09 and 0.12 percentage points， respectively， compared to the optimal baseline models such as ATLOP （Adaptive Thresholding and Localized cOntext Pooling） and E2GRE （Entity and Evidence Guided Relation Extraction）， indicating that DREERE model is able to acquire semantic information of entities in documents effectively.

As a classical problem in spatial databases， Aggregate Nearest Neighbor （ANN） query is of great importance in the optimization of network link structures， the location selection of logistics distribution points and the car-sharing services， and can effectively contribute to the development of fields such as logistics， mobile Internet industry and operations research. The existing research has some shortcomings： lack of efficient index structure for large-scale dynamic road network data， low query efficiency of the algorithms when the data point locations move in real time and network weights update dynamically. To address these problems， an ANN query algorithm in dynamic scenarios was proposed. Firstly， with adopting G-tree as the road network index， a pruning algorithm combining spatial index structures such as quadtrees and k-d trees with the Incremental Euclidean Restriction （IER） algorithm was proposed to solve ANN queries in statistic space. Then， aiming at the issue of frequent updates of data point locations in dynamic scenarios， the time window and safe zone update strategy were added to reduce the iteration times of the algorithm， experimental results showed that the efficiency could be improved by 8% to 85%. Finally， for ANN query problems with road network weight changed， based on historical query results， two correction based continuous query algorithms were proposed to obtain the current query results according to the increment of weight changes. In certain scenarios， these algorithms can reduce errors by approximately 50%. The theoretical research and experimental results show that the proposed algorithms can solve the ANN query problems in dynamic scenarios efficiently and more accurately.