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Abstract：The uncontrolled positioning accuracy of optical satellite is an important factor to determine the effect of image application. The research shows that the main factors of image optical satellite uncontrolled positioning accuracy include attitude measurement random error, time synchronization error, attitude low-frequency error caused by structure deformation, etc. There are many image factors which is difficult to decouple. The traditional method of establishing uncontrolled positioning error model through control point evaluation is difficult to objectively and comprehensively reveal the error law. In this paper, the convolutional neural network in introduced into the modeling of uncontrolled positioning accuracy. Taking the satellite image parameters, more comprehensively reveal the law of uncontrolled positioning error, and improve the uncontrolled positioning accuracy by predicting positioning error. Finally, the effectiveness and feasibility of this method are verified by using the 10019 images data of Luo-Jia 01 satellite.