The Innovation of “Six Steps” Teaching Model of College English under Data Chain Mode

Abstract

Abstract In this paper, under the data chain model, the form of envelope modulation is first utilized to identify the lexical feature quantity precision based on the horizontal energy clustering point of lexical distribution. Then, the reconstruction of the teaching model is accomplished by clustering assessment of modulation means and optimal experience through subtractive clustering. Finally, the multivariate array parameter distribution method is used to realize the node optimization deployment design of the data chain system, and at the same time, the teaching quality weight coefficients are obtained according to the overlap probability and the energy domain detection probability. After applying the six-step teaching model, the passing rate of the third level is about 40%, and the scoring rate of the experimental group is higher than that of the control group by 1.64%. When the vocabulary frequency is 200HZ, the P-value is 0.0039 less than 0.05 effect is significant, which can enhance the students’ subjective initiative, and the English teaching mode realizes a better innovative effect.