The purpose of this research is to investigate the effects and influential factors of computerized psychological treatments for depression. This research adopted the methods of meta-analysis and meta-regression analysis to carry out the literature search through four major data-bases as PubMed, PsycINFO, Embase and Web of Science, 50 literatures were included in the meta-analysis eventually, including 42 randomized controlled trials and 67 samples for effective meta-analysis. The sample for the research is 7920 participants, 4208 participants for computerized intervention group and 3712 participants for the control group, respectively. The results suggest that: (1) the overall effects of computerized psychological treatments for depression is 0.53, which is a medium effectiveness, the effectiveness for the subjects with 3 months tracking is 0.14, while the one with 6 months tracking is 0.16. (2) Significant differences are revealed in the subgroups analysis for the age group, depression severity, support method and measurement scales: With a more close look at the subjects, we can find out that the effectiveness for the youth group is relatively small as 0.24, while the effectiveness for adult group is relatively great as 0.59; the treatment effectiveness for the severe depression group is significantly higher than non-severe group (severe depression: d=0.73; non-severe depression: d=0.48, p=0.003). Significant differences are revealed in the treatment effectiveness for the 4 patterns of support (Face to face support group: d=0.58; Email support: d=0.70; Phone support: d=0.46; no support, d=0.40, p=0.038); Significant differences of the effectiveness are also revealed in the treatment effectiveness for the two different kinds of treatments in the measurement scales (BDI: d=0.63; CES-D: d=0.36, p=0.008); the differences are not significant in the analysis of the 3 subgroups as intervention pattern (network: d=0.54; stand-alone PC: d=0.38, p=0.13), intervention orientation (CBT: d=0.52; PST: d=0.48, p=0.55) and analytical methods (full treatment: d=0.53; intention for treatment: d=0.53, p=0.97). (3) In the total sample, the year of publication significantly affects the effectiveness of treatment (B=-0.0273, p<0.001); drop rate does not significantly affect the effectiveness of treatment for the total sample (B=0.0028, p=0.12). However, with a more complete categorization of the different samples in the measurement, we find out that drop rate significantly affects the treatment effectiveness for the CES-D samples (B=0.0106, p <0.001); intervention unit number significantly affects the effectiveness of treatment for the CES-D sample (B=0.0689, p=0.01), nevertheless, it plays no significant affects for the total samples(B=0.0016, p= 0.86) and BDI samples (B=-0.0313, p=0.08). (4) Publication bias may be seen in this study, but it is unreasonable to overthrow the existing conclusions. Funnel plot suggests that the publication bias may exist, the result from the Begg & Mazumdar’s rank correlation test shows that z=2.52, p<0.01; Egger's regression is significant (t=5.27, df=65, p<0.001), therefore, the two statistical results are significant. But the fail-safe number in the study is 7488. It means that 7488 contrary studies are required to be conducted to overthrow the existing meta-analysis conclusions. Conclusion: The computerized psychological treatments for depression manifests medium effectiveness; the age group, severity of depression, support method, measurement scale and the year of publication are of regulatory role for the overall effective of treatment. Therefore, the research on computerized psychological treatments for depression in the future should pay attention to these regulatory variables for their effectiveness to treatments.