Artificial Natural Language Semantic Mining Simulation Based on Graph Neural Network
Semantic mining tools can accurately extract useful information from bulk data of unstructured artificial natural language text.However,due to the semi-structured,multi-scale,massive,and complex association attributes of network environment texts,text data usually has high dimensions and only a small number of nodes have clear la-bels,making semantic mining difficult.In this article,a method of mining artificial natural language semantics based on graph neural network was proposed.Firstly,multi-head attention was combined with semi-supervised graph convo-lution neural network to reduce the dimension of artificial natural language text.Then,the improved fuzzy c-means clustering algorithm was combined with a partheno-genetic algorithm based on immune mechanism to construct an ar-tificial natural language semantic mining algorithm.Experimental results show that the clustering purity,accuracy and recall rate of the proposed method are higher than 95%,proving its application performance.
NETL
NSTL
万方数据
维普
