Research on the Reform of Ancient Chinese and Modern Chinese Language Teaching in the Context of Deep Learning

Abstract

Abstract In this paper, we use a deep learning algorithm to optimize the loss function of Chinese data, dynamically adjust the learning rate by gradient descent method, and adopt an exponential moving weighted average to deal with language information. The second-order information of the historical gradient is processed to calculate the change value of the objective function of the Chinese language variables, and the learning rate is scaled accordingly, which is inversely proportional to the teaching reform model. Ultimately, the error between the objective function value and the predicted value is optimized with a smaller learning rate, and the neuron learning rate is scaled to achieve the best solution for the language teaching reform of ancient Chinese and modern Chinese. The accuracy rate of the deep learning algorithm is verified to be as high as 0.9, which provides reliable technical support for teaching reform. The reform’s weighting of teaching content to 0.471 underscored its significance in teaching. Fifty-three percent of the students showed strong interest in the teaching method reform, highlighting the popularity of teaching mode innovation. The teaching rating before the reform increased by 6 points compared to that after the reform, showing the positive impact of the reform on teaching.