Objective To explore the trajectory changes of depressive symptoms and social functioning outcomes based on a prospective cohort study of depressive disorder， analyze its potential influencing factors， so as to provide a scientific basis for early clinical identification of patients with poor social functioning outcomes， and thereby timely adjust treatment plans to promote the comprehensive recovery of patients' social functioning. Methods From November 9， 2016 to December 30， 2020， a prospective multicenter cohort study on depressive disorder was conducted involving 1 385 participants from 12 hospitals selected across 10 provinces in China. General demographic data were collected at baseline， and depressive symptoms and social functioning were assessed at baseline， weeks 4， 8， 12， 16， and 24 using the 17-item Hamilton Depression Rating Scale （HAMD-17） and Sheehan Disability Scale（ SDS）， respectively. SAS（ PROC TRAJ） was used to construct latent class mixed models（ LCMMs）. The optimal trajectory model was identified by comparing models with different numbers of classes， including statistical indicators such as Bayesian information criterion（ BIC）， substantive indicators（ entropy）， and empirical indicators［ each class containing at least a sufficient number of participants （＞ 5%）］. Subgroups were then divided based on the initial level and slope direction of the trajectory. The chi-square test and Shapiro-Wilk test were used to analyze the baseline differences between groups， and the distribution percentage of social functioning trajectories within each symptom trajectory group was calculated to evaluate the degree of "symptom-function" matching. Patients with "rapid improvement in symptoms but slow or moderate improvement in function"， "moderate improvement in symptoms but slow improvement in function"， and "slow improvement in both symptoms and function" were collectively defined as "delayed functional recovery". The baseline differences between this group and the "good functional recovery" group were analyzed， and multivariate binary Logistic regression was used to identify independent influencing factors. Results A total of 1 385 patients were included in the analysis. The trajectories of depressive symptoms were divided into three subgroups： the rapid improvement group（ n=139， 10.04%）， the moderate improvement group （n=948， 68.45%）， and the slow improvement group（ n=298， 21.52%）. A comparison of baseline data showed that the slow improvement group had the lowest age［ 30.0（ 24.0， 41.0） years］， the highest proportion of students （20.54%） and single/divorced individuals（ 51.52%）， and the most severe depression［ HAMD-17 total score of 26.0（ 23.0， 29.0）］. The rapid improvement group had the highest age［ 33.0（ 26.0， 47.0） years］， the highest proportion of unemployed individuals（ 38.13%）， and the highest proportion of married individuals（ 63.04%）. The moderate improvement group had the mildest baseline depression［ HAMD-17 total score of 20.0（ 17.0， 23.0）］. All differences among these three groups were statistically significant（ all P＜0.05）. In the rapid improvement group， 100.00% of patients achieved rapid recovery of social functioning. There were 194 patients（ 14.01%） in the "delayed functional recovery" group. Binary Logistic regression analysis revealed that， after controlling for confounding factors such as age， gender， medication type， family history， seizure type， educational level， place of residence， marital status， and employment status， only the baseline HAMD-17 total score was a risk factor for delayed functional recovery in patients with depressive disorder［ OR=0.942， 95%CI（ 0.914， 0.971）， P=0.000 1］. Conclusions The symptoms and social functioning recovery of patients with depressive disorder show a trend of synchronous improvement overall. Improvement of early symptoms is the prerequisite for rapid functional recovery， while residual symptoms may hinder the recovery of social functioning. At the same time， this study identify employment（ or study） status， marital status， and age as important factors influencing symptom outcomes. Therefore， early identification of high-risk individuals and the implementation of targeted， enhanced interventions are crucial for improving prognosis.