With the increase of the number of college students, in order to improve the quality of teaching, the professional setting is more and more detailed, the course content is more and more rich, the information teaching system will be increasingly perfect. This paper proposes an improved genetic algorithm for English informatics education scheduling system to address the problem of the contradiction between the rapidly increasing number of students and the limited teaching resources in the current English scheduling in universities. The system first proposes an improved parallel mechanism which computes the probability of population changing evolutionary strategies according to the algebra of population maintaining the optimal solution invariant. The evolution strategy needed by the population can be obtained by fuzzy reasoning according to the evolution degree and individual difference of the population so as to get rid of the local extreme value and search for the global optimal value. Secondly, an improved algorithm initialization method, fitness transformation method, and competition strategy are proposed to adjust the balance between convergence speed and population diversity. Finally, based on Baldwin evolution theory, a new local search strategy is proposed. The algorithm has been tested on famous international data sets and a school’s English curriculum. Experimental results show that this algorithm has higher efficiency and quality than other algorithms with better performance, and the proposed teaching system can improve the teaching quality of English information education.

In order to solve the problem of the oral English auxiliary teaching system, a research based on Deep Learning was proposed. Based on the theory of Deep Learning, the teaching mode of Deep Learning for college students built on information technology was investigated in the research. As for Shallow Strategy, the item “I think that the best way to pass the English examination is to keep high frequency words in mind” changed most significantly from 3.3 to 2.68. As for Deep Strategy, the most significant change was the item “It can be very interesting for almost all the oral English topics as long as you engage in it actively.” The value increased from 3.31 to 3.97, which was almost close to 4. From the comparison, it could be found that the students changed from memorizing English words mechanically and passively in order to pass the exam to engaging in the oral English situation actively to solve problems so as to obtain self-satisfaction. As a language, English can reflect a unique cultural heritage. It is different from Chinese culture, which can improve learners’ spiritual and cultural accomplishments subtly. In addition, we should try to solve problems with English logical thinking, which can train our critical thinking and oral expression ability, enhance our self-confidence, and improve our sense of self-efficacy. A design-based research paradigm was used in the research. Through the integrated use of information technology, it aimed at building out a deeply mixed teaching mode based on “Cloud Class and Offline Class.” A combination of quantitative and qualitative data collection methods was adopted to evaluate the practical effect. According to the experimental results, two rounds of mixed-mode iterative loop design were performed for the mixed teaching mode. In order to make it more operable, it was improved and perfected continuously.