The use of intelligent college English writing training system will certainly promote the traditional teaching structure and realize a new and efficient English writing teaching mode. On the basis of machine learning and the herd effect algorithm, this article constructs an artificial intelligence-based English intelligent writing system. Moreover, in view of the shortcomings of traditional models and the characteristics of intelligent English writing, this paper proposes an improved algorithm for optimization of swarm particle walking paths. In addition, this article proposes a relative attractiveness to initialize the formation of small-scale groups based on the herd effect. Then, in the process of intelligent writing, by establishing an information sharing mechanism between groups, each group is continuously updated and reorganized according to the relative attractiveness of the group, so that the writing process can be simulated more realistically. From the experimental research, it can be seen that the model constructed in this paper has a certain degree of intelligence.
TommaselA. and GodoyD., A Social-aware Online Short-text Feature Selection Technique for Social Media, Information Fusion40(2) (2017), 1–17.
2.
NayakM. and NayakA.K., Odia Running Text Recognition Using Moment-Based Feature Extraction and Mean Distance Classification Technique, Advances in Intelligent Systems & Computing309(1) (2015), 497–506.
3.
RamalakshmiE. and GollaS., An Advanced Fuzzy Constructing Algorithm for Feature Discovery in Text Mining, International Journal of Computer Applications127(17) (2015), 30–34.
4.
GisselS.T., Scaffolding Students’ Independent Decoding of Unfamiliar Text with a Prototype of an eBook-feature, Journal of Information Technology Education Research14(2015) (2015), 439–470.
5.
OkiT., Scene Text Localization Using Object Detection Based on Filtered Feature Channels and Crosswise Region Merging, Growth & Change21(3) (2015), 61–76.
6.
KambleR.R. and KodavadeD.V., Relevance Feature Search for Text Mining using FClustering Algorithm, International Journal of Computer Sciences & Engineering6(7) (2018), 223–227.
7.
WuG., ZhaoM. and HanL., A Fingerprint Feature Extraction Algorithm based on Optimal Decision for Text Copy Detection, International Journal of Security & Its Applications10(11) (2016), 67–78.
8.
SoleymanpourM. and MarviH., Text-independent speaker identification based on selection of the most similar feature vectors, International Journal of Speech Technology20(1) (2016), 1–10.
9.
MojaveriyanM., EbrahimpourkomlehH. and MousaviradS.J., IGICA: A Hybrid Feature Selection Approach in Text Categorization, International Journal of Intelligent Systems Technologies & Applications8(3) (2016), 42–47.
10.
AghdamM.H. and HeidariS., Feature Selection Using Particle Swarm Optimization In Text Categorization, Journal of Artificial Intelligence & Soft Computing Research 5(4) (2015), 38–43.
11.
RobatiZ., ZahediM. and FarN.F., Feature Selection and Reduction for Persian Text Classification, International Journal of Computer Applications109(17) (2015), 1–5.
12.
ZiaT., AbbasQ. and AkhtarM.P., Evaluation of Feature Selection Approaches for Urdu Text Categorization, International Journal of Intelligent Systems Technologies & Applications07(6) (2015), 33–40.
13.
ZiaT., AkhterM.P. and AbbasQ., Comparative Study of Feature Selection Approaches for Urdu Text Categorization, Malaysian Journal of Computer Science28(2) (2015), 93–109.
14.
YanK., LiZ. and ZhangC., A New multi-instance multi-label learning approach for image and text classification, Multimedia Tools & Applications75(13) (2016), 7875–7890.
15.
KumarG. and VivekanandanK., Intelligent Model View Controller Based Semantic Webservice Call Through Mishmash Text Featuring Technique, Journal of Computational & Theoretical Nanoscience14(4) (2017), 2021–2029.
16.
ShiB., BaiX. and YaoC., An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition, ieee transactions on pattern analysis & machine intelligence39(11) (2017), 2298–2304.
17.
Al-TahrawM.M., Polynomial Neural Networks versus Other Arabic Text Classifiers, Journal of Software11(5) (2016), 418–430.
18.
HuY., MiliosE.E. and BlusteinJ., Document Clustering With Dual Supervision Through Feature Reweighting, Computational Intelligence32(3) (2016), 480–513.
19.
LinharesR.N., AlcântaraC.M.G., GonçalvesE.Á., et al., Teaching Evaluation by Teachers from Brazil and Portugal: A Comparative Analysis, American Journal of Educational Research5(5) (2017), 546–551.
20.
HuangN., Analysis and Design of University Teaching Evaluation System Based on JSP Platform, International Journal of Education & Management Engineering7(3) (2017), 43–50.
21.
MurciaJ.A.M., TorregrosaY.S. and BelandoN., Questionnaire evaluating teaching competencies in the university environment. Evaluation of teaching competencies in the university, Naer Journal of New Approaches in Educational Research4(1) (2015), 54–61.
22.
LiuS. and ChenP., Research on Fuzzy Comprehensive Evaluation in Practice Teaching Assessment of Computer Majors, International Journal of Modern Education & Computer Science7(11) (2015), 12–19.
23.
ZhouL., LiH. and SunK., Teaching performance evaluation by means of a hierarchical multifactorial evaluation model based on type-2 fuzzy sets, Applied Intelligence46(1) (2016), 1–11.
24.
OliverosM.A., GarcíaA. and ValdezB., Evaluation of a Teaching Sequence Regarding Science, Technology and Society Values in Higher Education, Creative Education6(16) (2015), 1768–1775.
