Active domain adaptation (ADA) aims to train an effective model under the context of domain adaptation with as few queried instances as possible. However, existing algorithms tend to select instances that are either uninformative, redundant, or outliers due to domain shift. To address this issue, a novel approach called neighbor environment perception sample selection (NEPS) for active domain adaptation is proposed. NEPS explores the target sample informativeness in a neighbor environment-aware manner to select instances that are potentially most valuable under domain shift. Specifically, from informativeness perspective, NEPS aims to acquire knowledge not only from individual data points but also from their neighboring samples. This is achieved by measuring neighbor awareness informativeness score (NAIS) , which ensures the selected samples have both high individual informativeness score and environment informativeness score. Additionally, NEPS ranks and selects samples based on their similarity scores with labeled samples to ensure diversity among the chosen instances. Furthermore, NEPS makes effective use of all labeled samples as well as a large amount of unlabeled data from the target domain to enhance the model's performance. Experimental results demonstrate that NEPS exhibits strong sample selection capability and outperforms existing models in terms of classification performance on various benchmark datasets.