Presently, many researchers are focusing on the English intercultural communication course. However, these courses face serious challenges, such as individual student variances in conventional English cross-cultural teaching, fostering students’ cross-cultural communication abilities, and enhancing teaching quality. These problems need to be solved; therefore, this paper aims to explore the development and application of the Massive open online courses (MOOCs) system in the English cross-cultural communication course based on neural networks. Firstly, the overall function modules of the MOOC system for the English intercultural communication courses are described, with emphasis on the student function module, teacher function module, administrator function module, and system database design of the MOOC system. Second, the MOOC system’s teaching technique in an English intercultural communication course is based on a genetic algorithm, and the MOOC system’s teaching quality index in an English intercultural communication course is chosen using principal component analysis. We conducted several tests to demonstrate that the MOOC system of the English intercultural communication course using the neural network suggested in this work is resilient and may successfully increase teaching quality. The experiment proves that the MOOC system of the English intercultural communication course based on the neural network developed in this paper has efficient results as compared to existing studies. In addition, it can effectively improve the teaching quality and can train students’ intercultural communication skills and their ability to adapt to intercultural communication.

The advent of the intelligence age has injected new elements into the development of literature. The synergic modification of Anglo-American (AAL) traumatic narrative (TN) literature by artificial intelligence (AI) technology and interactive design (ID) psychology will produce new possibilities in literary creation. First, by studying natural language processing (NLP) technology, this study proposes a modification language model (LM) based on the double-layered recurrent neural network (RNN) algorithm and constructs an intelligent language modification system based on the improved LM model. The results show that the performance of the proposed model is excellent; only about 30% of the respondents like AAL literature; the lack of common cultural background, appreciation difficulties, and language barriers have become the main reasons for the decline of reading willingness of AAL literature. Finally, AI technology and ID psychology are used to modify a famous TN work respectively and synergically, and the modified work is appreciated by respondents to collect their comments. The results corroborate that 62% of the respondents like original articles, but their likability scores have decreased for individually modified work by AI or ID psychology. In comparison, under the synergic modification efforts of AI and ID psychology, the popularity of the modified work has increased slightly, with 65% of the respondents showing a likability to read. Therefore, it is concluded that literary modification by single ID psychology or AI technology will reduce the reading threshold by trading off the literary value of the original work. The core of literary creation depends on human intelligence, and AI might still not be able to generate high-standard literary works independently because human minds and thoughts cannot be controlled and predicted by machines. The research results provide new ideas and improvement directions for the field of AI-assisted writing.