Research on the Application of Chinese Language Teaching System based on Artificial Intelligence Technology

This study builds a Chinese Language and literature majors teaching system, by combining artificial intelligence (AI) technology and performance, with improving the teaching effect. This paper implements a classifier-based fast calculation method for successive breaking of active power flow depend on existing coordination algorithms. This method provides high-quality active power information for the static safety check and control of successive breaking. Furthermore, to improve the reliability of the power flow results, this work accelerates computation, increases the operation effect of the teaching system, and utilizes the NR method for algorithm-sensitive errors. To assess the efficiency of the implemented teaching system, a simulated classroom setting was used for testing. The implemented method was compared with Random Forest (RF) and Gradient Boosting (GB) methods with time of 15
$s$
in AI-based vocabulary check, and 35
$s$
in AI-based grammar check, power of 0.25 W in AI-based vocabulary check, and 0.59 W in AI-based on grammar check.