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Frequency counting of complex patterns such as subtrees is more challenging than for simple itemsets and sequences, as the number of possible candidate patterns in a tree is much higher than one-dimensional data structures, with dramatically higher processing times. In this paper, we propose a new and scalable solution for frequent subtree mining (FTM) on the Automata Processor (AP), a new and highly parallel accelerator architecture. We present a multi-stage pruning framework on the AP, called AP-FTM, to reduce the search space of FTM candidates. This achieves up to 353X speedup at the cost of a small reduction in accuracy, on four real-world and synthetic datasets, when compared with PatternMatcher, a practical and exact CPU solution. To provide a fully accurate and still scalable solution, we propose a hybrid method to combine AP-FTM with a CPU exact-matching approach, and achieve up to 262X …