Content based image retrieval (CBIR) has been a practical solution to an image retrieval methodology based on image contents. But CBIR systems are generally subject to constraints due to the semantic gap between the low-level image descriptors and human perception. In this paper, to improve the retrieval results, a novel CBIR architecture and a Convolutional Neural Network (CNN), ConvNeXt, which is the leading CNN and relevance feedback (RF) is proposed. A highly performant deep ConvNeXt able to acquire high level image representations is used in order to extract discriminative features by projecting the images to meaningful embeddings that contain the visual characteristics of the image. With a view to the semantic gap, RF is implemented as a modality that the user can interactively annotate the retrieved images in terms of relevance or irrelevance. This feedback is repeatedly modified to drive the evolution of the feature space in such a way that the retrieval system is progressively able to provide satisfactory results in subsequent times. In accordance with the experimental results on test samples from the sample image data sets, the number shows significant performance gain with respect to the traditional CBIR methods, by combining with the RF of the ConvNeXt. By integrating deep learning for feature extraction and users feedbacks for adaption the proposed system can achieve a higher retrieval accuracy and users’ satisfaction. More importantly, this method leads to better and more responsive CBIR systems and handles the inherent problems of these systems as regards to the content of the image.