Full morphology of single neurons is indispensable to understand cell types, the basic building blocks in brains. Projecting trajectories are critical to extract biologically relevant information from neuron morphologies, as they provide valuable information of both connectivity and cell identity. We developed an artificial intelligence method, Deep Sequential Model (DSM), to extract concise, cell-type defining features from projections across brain regions. DSM achieves more than 90% accuracy in classifying twelve major neuron projection types, without compromising performance when spatial noise is present. Such remarkable robustness enabled us to efficiently manage and analyze several major full-morphology data sources, showcasing how characteristic long projections can define cell identities. We also succeeded in applying our model to both discovering previously unknown neuron subtypes and analyzing exceptional co-expressed genes involved in neuron projection circuits.