As data dimensions and volume rapidly increase, attribute reduction using the original data becomes computationally infeasible. Large data frequently contain various redundant attributes and types of noise. This leads to the problems of overfitting and inefficiency in data processing. To address these problems, this paper proposes a general sampling method for attribute reduction by introducing three-way decisions, namely, three-way sampling (3WS), which is the first sampling method that describes the decision boundary accurately while improving the data quality significantly. To improve the effectiveness and efficiency of attribute reduction, we designed a rapid attribute reduction method based on three-way sampling (3WS-RAR). The 3WS-RAR method consists of three main steps: data sampling, attribute reduction, and model effectiveness evaluation. For data sampling, we define the three regions of the 3WS using support vectors to describe the data and use the boundary region as the sampling results. For the attribute reduction, we compute the neighborhood self-information for each attribute while considering the upper and lower approximations. For the effectiveness evaluation, we conducted experiments on 15 relatively large-scale datasets and analysed the influence of parameters. The experimental results reveal that, compared with state-of-the-art attribute reduction models, 3WS-RAR performs better on public benchmark datasets.