Remote sensing image interpretation plays an important role in the acquisition of Land Use and Land Cover (LULC) information, and automatic classification serves as the key to improve the efficiency of LULC information acquisition. The actual scenes have a great mount of inaccurate prior knowledge. Extracting and integrating the available knowledge in the prior knowledge can help to further improve the accuracy, automation rate and scale application ability of image classification methods. Based on the above situation, a new deep learning classification method of Landsat 8 OLI images based on inaccurate prior knowledge was proposed. For the proposed method, inaccurate units in prior knowledge were avoided automatically, realizing automatic region selection and feature extraction of classified samples and obtaining high confidence knowledge in the constraint space of patches. Then, the deep residual network was trained by using these classified samples, and the accurate classification of large-area images was achieved. In the experiment, Xinbei district of Changzhou city was taken as the example, the data of 2009 land use status of this district was selected as the prior data, and the 2014 Landsat 8 OLI image of this district was selected as the to-be-classified image. The experimental results show that the proposed method has advantages such as the integration of inaccurate prior knowledge and the accurate classification of large-area contiguous LULC information. Besides, it can obtain the accurate boundary of main land use patches, and has the accuracy for patch classification in the whole image of 88.7% and the Kappa coefficient of 0.842.The proposed method can cooperate with deep learning method to achieve high precision Landsat 8 OLI remote sensing image classification.