An adaptive learning model of English vocabulary is developed, which contains a machine learning algorithm. The model records learners’ self-selection of what they learn to reflect individual differences. The key parameter of such a learning tool of dynamic modeling is conditional probability that measures the adaptive relationship between a cognitive feature and certain learning content. Therefore, this parameter is called adaptability. It is updated every time a learner self-selects the learning contents about a word, which is regarded as a time of training. The adaptability is constantly adjusted to modify and maintain the model through training. The model abstracts the problem to be solved, according to the adaptive test process based on the item response theory, into mathematical formulas with our reference to those in the AdaBoost algorithm. This model can continue to iterate the adaptability until it is stable and recommends proper learning contents for users. This paper first reviews relevant literature and talks about the value of this topic, then expounds on the theoretical basis, and focuses on the construction of the model with case study at last.