In order to fully explore the use of massive satellite network data, improve decision-making efficiency,and strengthen the analysis methods of spatial frequency and orbit resource acquisition and storage, especially for the GSO (geostationary satellite orbit) resource selection problem, a satellite network situation assessment strategy based on machine learning algorithm was proposed. By analyzing the characteristics of satellite network coordination factors, the CNN (convolutional neural network) was selected as the target algorithm model, and the training data set and label rules of the algorithm model were established. The data is reduced by the split information gain measurement method and a CNN evaluation model was established. Afterwards, a verification analysis was performed. Results show that the CNN model has a correct rate of 80% or more for the satellite network coordination situation assessment problem, and has high evaluation efficiency. Moreover, with the increase of the amount of data, the evaluation effect of CNN is gradually improved, which indicates the proposed method is an effective evaluation method for coordination situation analysis and resource reserve in satellite networks.