Research on Framework of On-demand Resource Matching Based on Deep Semantic Mining

Resource allocation is pivotal for human society. On-demand resource matching aims to efficiently assign resources to suitable demands based on the resource characteristics, which in widely applicable in area such as e-commerce, recruitment, and military operations. However, conventional matching methods like keyword matching lack semantic analysis, while manual rules have limited adaptability. With the emergence of deep learning and abundant matching data, deep semantic mining provides new concepts through using neural networks to extract semantic features, model associations, and understand text semantics. When applied to resource matching, it addresses the lack of semantic modeling in traditional methods, depicting semantic relationships between demands and resources. Thus, this paper proposes an on-demand resource matching framework based on deep semantic mining, exploring demand clarification, equivalence identification, rapid matching and accurate matching. It provides robust support for implementing on-demand resource matching research, which can lay the groundwork for advancing military and societal resource management.