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.

Nowadays， deep learning models have been widely applied in dialogue act recognition， which can improve classification performance by mining various features of dialogue acts. However， the existing methods neglect the latent association and interaction between different features of dialogue acts and also seldom consider the semantic relevance between labels of dialogue act in the classification process， which hinders from improving the performance of dialogue act recognition. To solve these problems， an MFA-HC （Multi-feature Fusion Attention-based Hierarchical Classification） method for recognizing dialogue act was proposed. Firstly， a hierarchical dialogue act classification framework based on learning without forgetting was proposed， which combined various fine-grained features such as words， parts of speech and relevant linguistic statistics to learn and train the dialogue act classification model. Secondly， a universality-individuality model based on attention mechanism was proposed to capture the universality and individuality features among different features. Experimental results on two benchmark datasets SwDA （Switchboard Dialogue Act corpus） and MRDA （ICSI Meeting Recorder Dialogue Act corpus） show that， compared with DARER （Dual-tAsk temporal Relational rEcurrent Reasoning network）， which has the current overall superior performance in existing methods， MFA-HC method improves the classification accuracy by 0.6% and 0.1% by capturing the universality and individuality features hidden in the utterance.