Research on Intelligent Calibration of English Long Sentence Translation Based on Corpus

With the continuous promotion and development of the new curriculum reform, English teaching is becoming more practical and comprehensive. As an indispensable part of daily English topics, the use rate and scope of English attributive clauses are extensive. Moreover, due to English attributive provisions, the length of the whole English sentence will inevitably increase; therefore, we can accurately understand and translate sentences by mastering the translation and understanding details of attributive clauses. In addition, there are noticeable differences between English and Chinese in attributive clauses. Chinese will not add a variety of modifiers like English but will directly put them in front of them as attributives, so we should pay attention to this in translation. This process increases the difficulty of the English translation. Therefore, this paper proposes a Corpus-based intelligent calibration of English long sentence translation. Based on the construction of the English long sentence Corpus, an intelligent calibration algorithm for English long sentence translation is designed, and experiments verify the effectiveness of this method.

Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform

The recent curriculum reform in China puts forward higher requirements for the development of physical education. In order to further improve students’ physical quality and motor skills, the traditional model was improved to address the lack of accuracy in motion recognition and detection of physical condition so as to assist teachers to improve students’ physical quality. First, the physical education teaching activities required by the new curriculum reform were studied with regard to the actual needs of China’s current social, political, and economic development; next, the application of artificial intelligence technology to physical education teaching activities was proposed; and finally, deep learning technology was studied and a human movement recognition model based on a long short-term memory (LSTM) neural network was established to identify the movement state of students in physical education teaching activities. The designed model includes three components: data acquisition, data calculation, and data visualization. The functions of each layer were introduced; then, the intelligent wearable system was adopted to detect the status of students and a feedback system was established to assist teaching; and finally, the dataset was constructed to train and test the designed model. The experimental results demonstrate that the recognition accuracy and loss value of the training model meet the practical requirements; in the algorithm test, the motion recognition accuracy of the designed model for different subjects was greater than 97.5%. Compared with the traditional human motion recognition algorithm, the designed model had a better recognition effect. Hence, the designed model can meet the actual needs of physical education. This exploration provides a new perspective for promoting the intelligent development of physical education.