Place recognition holds a pivotal influence in the field of computer vision. Feature pyramid, an advanced architecture introduced into place recognition, aims to produce features with richer semantic content. However, the existing methods ignore the efficient utilization of low-level features. To tackle this issue, we propose a novel place recognition architecture called the adaptive perspective-based fusion network (APFN). The main benefits of APFN lie in three aspects: 1) it adaptively optimizes the appropriate perspective and assigns the appropriate perspective-based weights dynamically for the multiscale low-level feature maps by a newly designed adaptive perspective-based attention (APA) module; 2) it effectively enhances the extracted low-level features and significantly shortens the transmission distance of low-level information; and 3) it enhances global information extraction via supervising the generation of high-level features by regularization. Extensive experiments on several public datasets validate the effectiveness of our method. APFN outperforms previous baseline methods by 1.6% points in average recall at top-1% (AR@1%) and 1.2% points in average recall at top-1 (AR@1) metrics.