Kleefstra syndrome is a disorder caused by a mutation in the EHMT1 gene characterized in humans by general developmental delay, mild to severe intellectual disability and autism. Here, we characterized cumulative memory in the Ehmt1 +/− mouse model using the Object Space Task. We combined conventional behavioral analysis with automated analysis by deep-learning networks, a session-based computational learning model, and a trial-based classifier. Ehmt1 +/− mice showed more anxiety-like features and generally explored objects less, but the difference decreased over time. Interestingly, when analyzing memory-specific exploration, Ehmt1 +/− show increased expression of cumulative memory, but a deficit in a more simple, control memory condition. Using our automatic classifier to differentiate between genotypes, we found that cumulative memory features are better suited for classification than general exploration differences. Thus, detailed behavioral classification with the Object Space Task produced a more detailed behavioral phenotype of the Ehmt1 +/− mouse model.