The Parkinson's disease (PD) is a heterogeneous neurodegenerative disease, of which the etiological and pathological mechanisms remain unclear to date. PD has been associated with diverse movement dysfunctions and non-motor symptoms (i.e., symptom heterogeneity) and progression patterns of these symptoms differ from patient to patient (i.e., progression heterogeneity). To address these, the present investigation aims at comprehensively considering full progression course of early PDs to identify subtypes, each of which can reflect unique PD progression pattern. We retrospectively analyzed the Parkinson's Progression Markers Initiative (PPMI) and the Parkinson Disease Biomarkers Program (PDBP) as the development and validation cohorts, respectively. An unsupervised deep learning model was built to model progression trajectories in diverse clinical manifestations and cerebrospinal fluid (CSF) biomarkers to produce a representation vector for each patient, encoding his/her symptom progression profile. Then by performing clustering analysis on the patients' representation vectors, we identified three subtypes with distinct PD progression patterns in the PPMI cohort: Subtype I, mild baseline severity and mild symptom progression; mild baseline severity and moderate progression; and Subtype III, rapid symptom progression. Replication in the PDBP validation cohort demonstrated reproducibility of the subtypes. After that, we explored demographic factors, CSF biomarkers, neuroimaging biomarkers in brain regional atrophy, and genetic factors of the subtypes. Last, to enhance usability of the subtypes, predictive model of subtypes that relies on data at baseline and 1-year follow-up was trained. In conclusion, the identified subtypes revealed significant symptom progression patterns of PDs. Patients with similar baseline severities can even suffer from different progression pattern, leading to distinct prognosis. Demographic factors, biomarkers, and genetic components of the subtypes suggested distinct biological mechanisms and pathways potentially leading to those progression patterns. Our findings may benefit pathophysiological study, clinical practice, and clinical trials to advance PD.