Online education has become an important way of learning English at present, and English vocabulary teaching can improve the efficiency of English vocabulary teaching through target visual detection. However, from the existing research, it can be seen that there are still some shortcomings in English vocabulary recognition. In order to improve the English vocabulary recognition effect, based on machine learning recognition technology, this study combines English vocabulary recognition needs of online education to construct an English vocabulary detection model based on convolutional neural network. The model takes the word’s overall feature as the feature extraction principle and adopts the analysis and extraction of the joint segment feature. Moreover, it discards the complicated process of first dividing a single letter and then performing feature extraction and recognition. In addition, this study design example tests to perform algorithm performance analysis. The experimental results show that the proposed algorithm model has certain effects, and it can be used as an auxiliary algorithm for online English vocabulary teaching.

Psychological changes will often play a huge influence on the teaching effects in the English vocabulary learning and teaching process. To further explore the relationship of psychological regulation with English vocabulary teaching, the hybrid collaborative recommender technology may be underlying to propose a hybrid algorithm for studying how the English vocabulary teaching effect is subjected to the psychological emotion factors as parameters added in the original similarity calculation method in the psychological regulation environment where the emotion is the core of study. In the end, the paper conducts the questionnaire survey among 300 students. Test data show that positive emotion acts as the catalyst of the English vocabulary teaching effect, having basically reached the expected goal of study.