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The spider silk protein has distinctive physical, chemical, mechanical and biological properties. As a functional material, the application value of spider silk has increased in many fields. Consequently, considerable progress has been made in the expression of recombinant spider silk proteins through many host systems by gene engineering techniques. However, the mechanical properties of the silk fibre spun with the recombinant spider silk proteins are unsatisfactory because the recombinant spider silk proteins have too low of a molecular weight and do not have molecular orientation. This paper describes the construction and expression of a quasi-spider silk protein composed of spider silk protein and collagen-like peptides with the Pichia pastoris expression system. The quasi-spider silk protein is an 'ABA-type' triblock copolymer composed of triple helix-forming A blocks at both ends of the middle section. The triple helix-forming A blocks at both ends of the triblock copolymers consist of (Pro-Gly-Pro) n homopolymeric stretches, and the middle section of the molecule (B section) contains a spider silk protein that has been optimally designed. The supramolecular structure created by the three-block copolymers through directed self-assembly ensures that the artificial spider silk fibres will meet the requirement for molecular weight and definite molecular orientation, thus promoting the formation of silk fibres. The authors hope that this project will contribute to the study of materials science and biomedical engineering in regards to the huge potential of spider silk protein in these fields.

Mouth shape identification helps oral English learners discover the features of their lip movements in English speaking, and correct their pronunciation more smoothly. So far, few scholars have applied image processing to identify mouth shape features of oral English learners. Most studies consider little about environmental factors, and ignore the changing mouth shape in pronunciation. Therefore, this paper explores the extraction and classification of mouth shape features in oral English teaching based on image processing. Firstly, an extraction and classification model were established for mouth shape features in oral English teaching. Then, the mouth shape images of oral English teaching were preprocessed. After that, the authors segmented the lips in oral English video frames based on neural network, extracted the lip boundaries from the said frames, and fitted them into curves. The proposed model was proved effective through experiments.

In the context of One Belt And One Road economic strategy, the increase of the international trade and Chinese and foreign cultural interaction mean that English practical abilities of contemporary college students should reach the higher standard. College oral English teaching model becomes the emphasis of college English teaching research.