Indoor regional localization is widely applied in the fields of medical care, smart buildings and so on. The most prominent problem in indoor area localization is the interference effect of the dynamic and unpredictable nature (such as multipath propagation, channel fading, etc.) of radio channel effect on the received signal strength(RSS). To reduce the radio interference, this paper proposes a new CNN-BiLSTM indoor region localization model of attention-based mechanism, which reduces the dependence of RSS sequence on channel variation by building the relationship between the coarse-grained features and location regions. Above all, the convolutional neural network (CNN) is used to acquire the characteristics of the RSS sequence to extract the fine-grained features of the regional center point. Then, the storage memory characteristics of bidirectional long short-term memory (BiLSTM) is applied to learn the coarse-grained features of the implied region scope in the current and past RSS sequences. Finally, when the attentional mechanism and the fused coarse-grained features are applied, the mapping relationship between RSS sequence features and regional locations is built, and the regional location information is obtained. In real indoor environment,the experimental results of regional location show that compared with the grid region comprehensive probability location model with the best positioning effect,the proposed method improves the accuracy and adaptability of regional location while reducing computational complexity.