Intelligent Calibration Method of Urban Publicity Translation Based on Machine Learning

Considering the problems of low accuracy and recall and the large KSMR value of traditional methods in urban publicity translation calibration, an intelligent calibration method of urban publicity translation based on machine learning is proposed. By extracting the words in the candidate vocabulary set of urban publicity translation, calculate the semantic correlation between the features of urban publicity translation text, establish the relationship between semantic information entropy and word weight value, determine the semantic weight of each feature, and calculate the amount of information provided by each urban publicity translation text feature in translation, Select a large amount of information as the recognition feature of urban publicity translation text, smooth the urban publicity text by using the change of the frequency of urban publicity translation equipment based on machine learning, and construct the urban publicity translation model. Before inputting the machine learning algorithm, the translation corpus needs to be preprocessed, and the matching probability of urban publicity text is calculated. Combined with the design of intelligent calibration steps of urban publicity translation, the intelligent calibration of urban publicity translation is realized. The experimental results show that the proposed method can not only improve the accuracy and recall of urban publicity translation calibration but also reduce the difficulty of translation in terms of the KSMR value.