The invention discloses a convolutional neural network-based remote sensing image target detection method, and mainly solves the problems that a remote sensing target with an ambiguous appearance cannot be well identified and enough target semantic information cannot be obtained in the prior art. The method comprises the following implementation steps: 1, collecting remote sensing images to construct a data set, and dividing the data set into a training set and a test set; 2, constructing a network model, wherein the model comprises a feature extraction sub-network, an RPN candidate box generation network, a context information fusion sub-network and a multi-region feature fusion sub-network; 3, training the model by using the training set until the number of iterations of training is equal to a preset number of terminations; and 4, inputting the test image into the trained model to obtain a target detection result. The method can strengthen the expression capability of the characteristics, enriches the semantic information of the target, enables the target to have more identifiability, improves the detection precision, and can be used for resource exploration, disaster monitoringand remote sensing image target detection of urban planning.