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In order to improve the management efficiency of college students' associations, the author combined with ant colony algorithm to study the student management model of college student associations. Firstly, based on the study of the basic idea, principle, process and application scope of ant colony algorithm under the framework of swarm intelligence, several improved versions of the well-known ant colony algorithm are studied in depth, it provides a basis and reference for the research work of core theory and practical engineering application, secondly, the author will introduce the theoretical framework of the diagnosis and evaluation of college student associations. Through extensive literature review, documents, conducted a questionnaire survey in the student associations of colleges and universities in Dalian, and collected a large number of detailed first-hand materials. The model obtained by using ant colony algorithm plays an important role in management.

In order to evaluate the success rate of students’ entrepreneurship more effectively based on a nonlinear differential equation, the author proposes a model for evaluating the success rate of students’ entrepreneurship based on a nonlinear differential equation. These models allow for a score set on the cost of higher education entrepreneurship and determine all higher education entrepreneurship based on it, realizing the cost optimization of higher education entrepreneurship. The simulation results show that the model has scoring and improved student entrepreneurship.

In order to understand the current achievements of vocational students' entrepreneurship, the author creates a model of university students' innovativeness and entrepreneurship. This study analyzes the quality requirements of stakeholders, such as schools and students, for higher education innovation and entrepreneurship education based on research and practical results obtained in Finland and abroad. With the help of probability theory statistics, the relevant indicators are classified, the innovation and entrepreneurship system of vocational educational institutions is built, and the evaluation standards for each indicator are presented. Based on this, the weight of each index is determined and the innovation and entrepreneurship education model of vocational colleges is built. Through the analysis of index scores and total scores at all levels of X Vocational and Technical College and J Vocational College, the total score for innovation and entrepreneurship education at X Vocational College is 3.307 and the total score for innovation and entrepreneurship is 3.307. The education of the J vocational and technical college is 2.743, so the applicability of the model is good.

Deep rooting is an important trait in rice drought resistance. Genetic resources of deep-rooting varieties are valuable in breeding of water-saving and drought-resistant rice. In the present study, 234 BC2F7 backcross introgression lines were derived from a cross of Dongye 80 (an accession of Dongxiang wild rice as the donor parent) and R974 (an indica restorer line as the recurrent parent). A genetic linkage map containing 1 977 bin markers was constructed by ddRADSeq for QTL analysis. Thirty-one QTLs for four root traits (the number of deep roots, the number of shallow roots, the total number of deep roots and the ratio of deep roots) were assessed on six rice chromosomes in two environments (2020 Shanghai and 2021 Hainan). Two of the QTLs, qDR5.1 and qTR5.2, were located on chromosome 5 in a 70-kb interval. They were detected in both environments. qDR5.1 explained 13.35% of the phenotypic variance in 2020 Shanghai and 12.01% of the phenotypic variance in 2021 Hainan. qTR5.2 accounted for 10.88% and 10.93% of the phenotypic variance, respectively. One QTL (qRDR2.2) for the ratio of deep roots was detected on chromosome 2 in a 210-kb interval and accounted for 6.72% of the phenotypic variance in 2020. The positive effects of these three QTLs were all from Dongxiang wild rice. Furthermore, nine and four putative candidate genes were identified in qRDR2.2 and qDR5.1/qTR5.2, respectively. These findings added to our knowledge of the genetic control of root traits in rice. In addition, this study will facilitate the future isolation of candidate genes of the deep-rooting trait and the utilization of Dongxiang wild rice in the improvement of rice drought resistance.