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How to efficiently manage and tune the performance of multimodel databases is still a problem. Therefore, in this study, we present a configuration parameter tuning tool MMDTune+ for ArangoDB. First, the selection of configuration parameters is based on the random forest algorithm for feature selection. Second, a workload\u2010aware mechanism is based on k\u2010means++ and the Pearson correlation coefficient to detect workload changes and match the empirical knowledge of historically similar workloads. Finally, the ArangoDB configuration parameters are optimized based on the improved TD3 algorithm. 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