In　this　paper,　a　bi-projection　neural　network　for　solving　a　class　of　constrained　quadratic　optimization　problems　is　proposed.　It　is　proved　that　the　proposed　neural　network　is　globally　stable　in　the　sense　of　Lyapunov,　and　the　output　trajectory　of　the　proposed　neural　network　will　converge　globally　to　an　optimal　solution.　Compared　with　existing　projection　neural　networks　(PNNs),　the　proposed　neural　network　has　a　very　small　model　size　owing　to　its　bi-projection　structure.　Furthermore,　an　application　to　data　fusion　shows　that　the　proposed　neural　network　is　very　effective.　Numerical　results　demonstrate　that　the　proposed　neural　network　is　much　faster　than　the　existing　PNNs.