Memory tiering is a solution to classify the increasing data for real-time analysis in hierarchical storage management based on its usage patterns and importance. However, traditional storage tiering methods that utilize static thresholds or heuristics are rigid and inefficient, while most existing AI methods use overly complex prediction models, which cost a lot of resources in training and classifying. To address these issues, we propose RABBIT, a Rarely Accessed Blocks Based Intelligent Tiering method for tiered-aware deduplication memory systems. It introduces a monitoring process which can track file-level access features in real systems, predicts which memory layer should the data enter with a daily updated decision tree model, and dynamically migrates the chosen data in a corresponding post-process with error correction mechanism. Meanwhile, a block-level global deduplication scheme is employed to ensure that only unique data blocks occupy space in the entire tiered memory system and save storage resources. We build a three-layers and four-layers architecture to evaluate our RABBIT design with block-level I/O traces and real-world workloads and prove RABBIT to be a highly scalable method. Compared with state-of-the-art storage tiering methods, our experimental results show that RABBIT can reduce the average per block latency by 16.89% to 21.84% with a higher access hit rate and save storage capacity by 8.7% to 21.4% for learning access features at the block-level and file-level, respectively.