Weakly　Supervised　Video　Salient　Object　Detection　(WSVSOD)　only　requires　coarse-grained　manual　annotations,　which　can　achieve　a　good　trade-off　between　labeling　efficiency　and　detection　performance.　In　this　paper,　a　Multiple　Pseudo　Label　Aggregation　Network　(MPLA-Net)　is　proposed　for　WSVSOD.　Firstly,　the　video　frames　that　can　obtain　high-quality　pseudo　labels　are　selected　to　generate　multiple　pseudo　labels,　so　as　to　avoid　the　prejudice　of　the　single　label.　Moreover,　the　pseudo　label　with　fine　edge　information　is　used　to　generate　the　Edge　Information　Map　(EIM).　Secondly,　MPLA-Net　is　designed　to　adequately　excavate　and　utilize　the　comprehensive　saliency　cues　in　multiple　pseudo　labels　to　improve　the　detection　accuracy,　in　which　ResNet-50　is　adopted　as　the　backbone　network.　Edge　loss,　pseudo　label　loss,　self-supervised　loss　and　fusion　loss　are　exploited　to　jointly　supervise　and　optimize　the　network　training　to　obtain　a　robust　detection　model.　Experimental　results　on　five　benchmark　datasets　demonstrate　that,　compared　with　existing　weakly　supervised　methods,　the　proposed　method　can　achieve　state-of-the-art　detection　accuracy　with　less　model　parameters　and　higher　detection　speed.　And　the　detected　salient　objects　have　fine　boundaries.