Image processing technics is usually applied to extract typical objects in Chemical Industry Parks （CIPs）. However， its precision is considered not enough for the monitoring and management of CIPs. The purpose of this study is to explore the feasibility of deep learning methods in the extraction of typical objects in CIPs. This study applied convolutional neural network BASS-Net to build a typical objects recognition model of CIPs through high-resolution remote sensing images. The results showed that the overall recognition accuracy， recall rate and F1 score of the BASS-Net model for typical objects in CIPs are 97.17%，97.76% and 97.46%， and the accuracy， recall rate and F1 for each 18 typical types can reach more than 93%， which indicated that the BASS-Net trained model has the ability to classify all the typical classes in CIPs. After comparing the results with those of the RF and SVM， it can be concluded that the BASS-Net model is far superior than the other two models. The BASS-Net model can be expected to provide support for environmental monitoring and management in CIPs.