Critical　node　detection　in　dynamic　networks　is　of　great　value　in　many　areas,　such　as　the　evolving　of　friendship　in　social　networks,　the　development　of　epidemics,　molecular　pathogenesis　of　diseases　and　so　on.　As　for　detecting　critical　nodes　in　dynamic　Protein-Protein　Interaction　Networks　(PPINs),　there　are　mainly　two　challenges:　the　first　is　to　construct　the　dynamic　PPINs　that　are　not　available　directly　from　biological　experiments　in　laboratories;　and　the　second　is　how　to　identify　the　most　critical　units　that　are　responsible　for　the　dynamic　processes.　This　paper　proposes　effective　framework　to　tackle　these　two　problems.　First　of　all,　this　paper　proposes　to　construct　the　dynamic　PPINs　by　simultaneously　modeling　the　activity　of　proteins　and　assembling　the　dynamic　co-regulation　protein　network　at　each　time　point.　As　result,　more　comprehensive　dynamic　PPINs　are　built.　Besides,　a　novel　critical　protein　detection　method　that　integrates　multiple　PPI　networks　into　a　Deep　Belief　Network　model　(referred　to　as　MIDBN)　is　developed.　The　integrated　model　is　trained　to　get　hierarchical　common　representations　of　multiple　sources　which　are　used　to　reconstruct　the　original　data.　The　variabilities　of　the　reconstruction　errors　across　the　time　courses　are　ranked　to　finally　get　the　top　proteins　that　have　significantly　different　evolving　structural　patterns　than　the　other　nodes　in　the　dynamic　networks.　We　evaluated　our　network　construction　method　by　comparing　the　functional　representations　of　the　derived　networks　with　that　of　two　other　traditional　construction　methods,　and　our　method　achieved　superior　function　analysis　results.　The　ranking　results　of　critical　proteins　from　MIDBN　were　compared　with　results　from　two　baseline　methods　and　the　comparison　results　showed　that　MIDBN　had　better　reconstruction　rate　and　identified　more　proteins　of　critical　value　to　yeast　cell　cycle　process.