In recent years, the proposed Deep Interest Network (DIN), Deep Interest Evolution Network (DIEN) and Deep Session Interest Network (DSIN) have further developed click-through rate prediction models. The above three models mainly focus on the evolution and development of the user’s historical behavior sequence. To a certain extent, the influence of environmental vectors on the user’s choice of the advertisement for the item to be recommended is ignored. As a result, click-through rates cannot be predicted more accurately when items have strong environmental attributes. To solve this problem, we propose a new model based on DIN, called Deep Interest Context Network (DICN). DICN combines two local activation units. It adaptively learns the user’s interest representation from the user’s historical behavior data concerning an advertisement and the context in which the advertisement is located (i.e., environmental factors). The experimental results show that DICN significantly improves the performance and model expression ability of advertisements with strong environmental attributes.