Knowledge Graph （KG） can extract and structurally represent the prior knowledge from massive data， and plays a key role in the construction and application of intelligent systems. Knowledge Graph Completion （KGC） aims to predict missing triples in the KGs to improve integrity and usability， and usually covers encoding and prediction links. However， the traditional KGC methods have difficulties in utilizing additional information and semantic information effectively in the encoding process， the problems of incomplete knowledge coverage and closed world in the prediction process， and the framework of first encoding and then prediction will be limited by embedded representation forms and computing efficiency. Large Language Models （LLMs） can solve these problems with rich knowledge and strong understanding abilities. Therefore， LLM methods for KGC were reviewed. Firstly， the basic concepts and research status of KGs and LLMs were outlined， and the KGC process was explained. Secondly， the existing KGC methods based on LLMs were summarized and sorted out from three aspects： using LLM as an encoder， using LLM as an generator， and basing on prompt guidance. Finally， the performance of the models on different datasets was summed up and the problems and challenges faced by KGC research based on LLMs were discussed.

To address the complexity and dynamic nature of vehicle trajectory anomaly detection in intelligent transportation systems， a novel method named DSTGRU （Dynamic Spatio-Temporal Gated Recurrent Unit） was proposed on the basis of Multi-level Spatio-Temporal Interaction dependency Dynamic Graph （MSTIDG）. In DSTGRU， by constructing dynamic graphs for short-term and long-term spatio-temporal interaction dependencies， the complex interactions between vehicles were captured effectively. In this process， the Multi-level Spatio-temporal interaction feature Fusion Bidirectional Gate Recurrent Unit （MSF-BiGRU） module was introduced to fuse multi-level spatio-temporal features， so as to integrate spatio-temporal information at different scales， thereby alleviating conflicts in shared information extraction and enhancing the model’s robustness， which improved the ability to identify anomalous trajectories. Experimental results demonstrate that DSTGRU outperforms the existing methods significantly on the TrackRisk and HighD datasets， achieving Pre@100 of 0.90 and 0.89， respectively， and AUROC of 0.913 and 0.827， respectively. Compared to existing advanced methods， DiffTAD and ImDiffusion， DSTGRU ranks first in multiple evaluation metrics. Additionally， DSTGRU exhibits strong robustness in complex scenarios， and identifies anomalous behaviors accurately， providing a solution for trajectory anomaly detection in intelligent transportation systems.

The existing researcher redeployment after discipline revocation relies on manual decision-making， which makes it difficult to coordinate discipline associations effectively. In this context， Large Language Model （LLM） with strong knowledge analysis capabilities provides new ideas for discipline optimization based on researcher redeployment after discipline revocation. However， on university research data represented by scientific research information， they face challenges such as difficulty in understanding professional terms and obvious long-tail distribution. Therefore， a multistage coupled decision-making framework for the redeployment of researchers after discipline revocation， namely MCRF （Multistage Coupled Redeployment Framework）， was proposed. MCRF was composed of four stages： recall， semantic enhancement， pairing， and reordering， and was able to decompose difficult problems into multiple relatively simple sub-problems effectively. Firstly， a discipline research word cloud association dataset was constructed to alleviate the problem of general models’ difficulty in understanding specialized academic terms. Secondly， an association recall algorithm was designed to recall Top-K related disciplines of scientific research information quickly， thereby reducing the overall decision-making time overhead. Finally， an implicit optimization module was introduced to generate diverse representations of scientific research information， thereby ensuring that tail discipline research information was able to be fully associated with researchers’ research directions， and accurate semantic matching was achieved through a fine-grained scientific research project ordering model. Experimental results show that on multiple datasets， the recall of the proposed framework reaches 92% in the recall stage， and the accuracy of the proposed framework is 96% in the reordering stage， verifying the effectiveness of MCRF in the task of discipline structure optimization effectively.