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With the development of the network, speech recognition technology has been widely used in e-commerce and other online platforms. But with the gradual growth of the network scale, facing the huge amount of information, it is very difficult for users to obtain the desired information in a short time, especially in the field of relatively high timeliness requirements (Gutierrez et al., 2020). Therefore, it is of great significance to study multi-dimensional trust data speech recognition algorithms under directional information recommendation, which can recommend excellent resources for users in a short time. Given the disadvantages of the current algorithm, this paper proposes a new multi-dimensional trust data speech recognition algorithm under directional information recommendation, calculates the user similarity based on the comprehensive trust value, repeatedly selects the clustering center, and obtains the user clustering results (Kumar & Aggarwal, 2020). Speech recognition, also known as automated speech recognition (ASR), computer speech recognition, or speech-to-text, is a feature that allows computer software to convert human speech into written text. While voice recognition and speech recognition are sometimes conflated, speech recognition focuses on converting speech from a verbal format to a written format, whereas voice recognition just aims to recognize an individual user's voice (Kanisha et al., 2018). Multipath interference is a wave physics phenomenon in which two or more components of a wave interact constructively or destructively when it travels from a source to a detector through two or more routes. According to the score of the nearest neighbor to the target resource, the target user can predict the target project. The implementation process of multi-dimensional trust data speech recognition algorithm based on directional information recommendation is given (Liu et al., 2020). A recommendation system is a sort of data filtering system that tries to predict how a user would evaluate or prefer a certain item. In simple words, it's an algorithm that suggests related products to customers. Experimental results show that the algorithm has good performance and high application value.
It is more and more difficult to extract the relevant information from the huge data, due to the rapid development of the internet. So, in this paper, an intelligent recommender of mobile wireless communication information based on voice recognition technology is proposed under high multipath interference. The suggested path is chosen based on various forms of mobile communication data, and the data is denoised to decrease the interference value of intelligent suggestion and assure the recommendation impact.