With the rapid development of information technology, the online education environment has become a reality. In specific online education practices, most teachers exhibit effective teaching behaviors by using multimedia technologies positively to stimulate the learning initiative of students, improve their learning performances, and promote their knowledge transfer. Based on theories of knowledge transfer and effective teaching behaviors, a measurement model that focuses on the effects of effective teaching behaviors of teachers on knowledge transfer of learners was constructed in this study. Moreover, the influencing mechanism of effective teaching behaviors of teachers (clear teaching, diversified teaching, task orientation, and guidance of student engagement) on knowledge transfer of students was discussed through questionnaire survey and multiple regression analysis. In addition, differences in knowledge transfer caused by different online learning platforms were analyzed. Results demonstrate that for the designed questionnaire, the general Cronbach’s α=0.796 and KMO values 0.820, indicating that the designed questionnaire has very good reliability and validity. Four aspects of clear teaching, diversified teaching, task orientation, and guidance of student engagement have significant positive correlations with knowledge transfer. Diversified teaching, task orientation, and guidance of student engagement can also promote knowledge transfer of students significantly. According to the Kruskal-Wallis test statistics, online learning platforms have significant effects on the knowledge transfer at the 0.05 level. Research conclusions have important references to recognize components of effective teaching behaviors of teachers, promote knowledge transfer improvement of students comprehensively, and facilitate the positive transformation of teachers to online teaching modes and strategies.

In order to improve the effect of animal husbandry economic analysis, this article studies the animal husbandry economy based on system dynamics and studies how to define total factor productivity and its measurement method. Moreover, this article compares and analyzes the production function method, data envelopment analysis, and index method for measuring total factor productivity, selects decision-making units, and determines and processes input-output data. In addition, this article combines the system dynamics model to explore the causal relationship of the animal husbandry economy and builds an intelligent model to intelligently analyze the animal husbandry economy. Finally, this article analyzes the economy and performance of animal husbandry based on simulation experiments. The simulation test results show that the system dynamics model proposed in this article has a good performance in the economic analysis of animal husbandry.

In order to improve the effect of agricultural economic risk forecast, this paper studies the agricultural economic risk forecast combined with data mining technology and builds an intelligent agricultural economic risk forecast system. Moreover, this paper employs a dynamic factor model to estimate common factors that drive changes in target topics. In order to construct a sentiment index that can reflect the overall operating situation of the macroeconomy, this paper improves the agricultural economic risk mining algorithm and standardizes the sentiment value corresponding to the target theme. In addition, this article analyzes the sentiment changes of its individual topics one by one in combination with the specific economic environment. The simulation study shows that the agricultural economic risk forecast system based on data mining technology proposed in this paper has a good effect.