The invention discloses a remote sensing image target detection method based on random access memory, which is based on SSD network, maximum posteriori distribution, Fisher information, Laplace approximation and other methods. The method comprises the following specific steps: 1, reading image data and preprocessing the data; 2, constructing a convolutional neural network based on a fusion featurepyramid; 3, training the convolutional neural network to obtain static model parameters; 4, proposing a random access memory idea; and 5, detecting a remote sensing image target, and dynamically updating model parameters. According to the invention, a remote sensing image multi-class target detection algorithm under a unified framework is proposed to adapt to detection of the remote sensing imagetarget, and a random access memory thought is proposed by using maximum posteriori distribution to dynamically adjust model parameters in a detection stage, so that the model has good adaptive capacity and generalization capacity to new data. Remote sensing images are input, target category and position information are output, the automation degree is high, the efficiency is greatly improved, andthe cost is reduced.