Research on Blended Teaching of Flipped Classroom Based on CNN-SSA-Bi-LSTM Deep Learning Model Computer Media

Aiming at the problem that the influencing factors of computer media flipped classroom hybrid teaching lead to the teaching effect not reaching the expected, this study proposes an ultra-short-term prediction model based on CNN-SSA-Bi-LSTM. CNN-SSA-Bi-LSTM is used to flip the study of mixed teaching in the classroom. This method constructs a one-dimensional convolutional neural network, performs data fusion and feature transformation on multiple key variables, and then constructs a two-way long-term short-term memory network prediction model, which realizes a 45-minute classroom for ultra-short-term prediction of the future. In addition, data optimization is performed through SSA to improve the predictive effect of the CNN-Bi-LSTM model. Experimental results show that compared with the traditional machine learning method, the proposed prediction model can effectively improve the prediction accuracy of the ultra-short-term classroom effect, and the relative variance of the continuous model is increased by 16.22%. High prediction accuracy and low error prove that CNN-SSA-Bi-LSTM deep learning model has strong application prospects in the research of flipped classroom hybrid teaching.