Mining and Optimization Strategies for Improving the Teaching Ability Path of Chinese Language and Literature Based on Big Data Analysis

Abstract

Teachers must receive pedagogy training, curriculum updates with a variety of texts, and the integration of interactive methods in order to improve the teaching skill path for Chinese language and literature. In order to promote a dynamic and all-encompassing approach to education in the sector, emphasis is focused on language competency, cultural awareness, and technology integration. In this manuscript, Mining and Optimization Strategies for Improving the Teaching Ability Path of Chinese Language and Literature Based on Big Data Analysis (MOS-ITACL-BDA-FBPINN) is proposed. Initially input datas are gathered from Chinese MNIST in CSV Dataset. To execute this, input data is pre-processed using Regularized Bias-Aware Ensemble Kalman Filter (RBAEKF) and it is used to identify the missing datas, from the dataset. Then the pre-processed datas are given to FBPINN for improve the intelligence level of teaching ability of the Chinese Language. In general, FBPINN does not express adapting optimization strategies to determine optimal parameters to ensure accurate intelligence level of teaching ability improvement. Hence, the Binary Battle Royale Optimizer Algorithm (BBROA) to optimize FBPINN which accurately improved the teaching ability. Then the proposed MOS-ITACL-BDA-FBPINN is implemented in Python and the performance metrics like Accuracy, Precision, Recall F1-Score, and Area under the Curve (AUC) Score are analysed. Performance of the CTN-BMS-FRP approach attains 18.75%, 26.89% and 32.57% higher accuracy; 16.87%, 24.57% and 32.94% higher Precision and 18.43%, 25.64% and 31.40% higher Recall when analysed through existing techniques likedisc ussing Retracted: Intelligent Analysis and Application of Preschool Education Language Teaching Quality Based on Deep Neural Network (IAA-PELTQ-DNN), the Convolution Neural Network-Assisted Strategies for Improving Teaching Quality of College English Flipped Class (TQ-CEFC-CNN), and Construction of Chinese Language Teaching System Model Based on Deep Learning under the Background of Artificial Intelligence (CTN-CLTS-ANN), methods respectively.