Although collaborative filtering(CF) can realize the personalized recommendation of users, most of the CF and their improved models do not consider the characteristics of users and items, hence they cannot explore the nonlinear relationship among samples. Compared with the CF, although deep learning can mine rich patterns of user interests, the network topology is based on two-way decision-making, ignoring the difficulty of recommended samples.To enhance the nonlinear expression of the model and distinguish the difficulty of the recommended samples, a personalized recommendation model based on sequential three-way decision with single feedforward neural network(STWD-SFNN-PR) is proposed. Firstly, to map high-dimensional sparse feature vectors to low-dimensional dense feature vectors, the STWD-SFNN-PR adopts embedding technology for feature preprocessing. Secondly, the characteristics of users and items are learned in the incremental network structure, Adam is employed to optimize network parameters, and samples that are difficult to recommend are returned. Thirdly, sequential three-way decision-making is utilized to increase the strategy of delayed decision-making, and the sequential threshold parameters are adopted at different granularity levels, thus realizing the classification of the recommended samples dynamically. Finally, to verify the feasibility and effectiveness of the model, a variety of movie recommendation datasets are selected for research, and the classic neural network recommendation model, the classic deep learning recommendation model, and the latest CF recommendation models with three-way are compared. Experimental results show that the STWD-SFNN-PR has better recommendation quality.