Research on adaptive conversion of AI language based on rough set

Abstract

In order to solve the problems of high complexity and low computational efficiency in traditional artificial intelligence (AI) language conversion methods, an adaptive AI language conversion method based on rough set is proposed. AI language preprocessing is realised by pre-emphasising, adding window, frame processing and endpoint detection. The attribute reduction algorithm based on rough set theory is used to select the features of AI language. The dimension of input feature vector is reduced. The experimental results show that after feature extraction, the computational efficiency is obviously improved, and the efficiency of the proposed method is the highest, averaging close to 100%. Compared with the traditional method, the complexity of the proposed method is lower, and the average complexity is 1.68% during the ten experimental iterations. This method simplifies the adaptive conversion process of AI language and has high computational efficiency.