To　forecast　the　parameters　of　surface　topography　under　different　processing　methods,　a　prediction　method　for　parameters　of　surface　topography　was　proposed　based　on　hybrid　Restricted　Boltzmann　Machine　(RBM).　Aiming　at　the　problems　that　the　generalization　ability　of　RBM　was　poor　and　the　fixed　training　rate　was　unfavorable　for　the　network　to　be　free　from　the　minimal　point,　a　sparse　autoencoder　was　utilized　to　extract　the　features　of　prediction　values,　and　a　hybrid　RBM　neural　network　was　designed　to　predict　the　parameters　values　of　surface　topography.　In　unsupervised　training,　a　principle　of　dynamic　learning　rate　was　employed　to　improve　the　network　so　as　to　increase　the　accuracy　of　eigenvector　mapping.　For　the　purpose　of　improving　the　training　speed　at　unsupervised　learning　stage,　the　rule　of　comparison　and　dispersion　was　adopted　to　conduct　the　rapid　training　of　neural　network.　Through　using　the　training　model　of　hybrid　RBM,　the　parameters　of　surface　topography　on　joint　surface　could　be　obtained　by　arbitrarily　inputting　machining　parameters.　To　guarantee　that　the　parameters　of　joint　surface　could　be　directly　applied　to　engineering,　based　on　the　parameters　of　surface　topography,　the　authors　the　actual　process　of　contact　model　application　was　deduced　with　fractal　theory.　In　this　way,　the　stiffness　and　damping　value　of　each　node　with　uneven　load　under　the　microstate　of　joint　surface　were　introduced　to　a　finite　element　model.　Compared　with　the　experimental　values　of　the　same　specimen,　the　correctness　of　implementation　method　for　joint　surface　was　proved,　which　could　provide　a　basis　for　optimizing　the　structure　and　improving　the　precision　of　numerically-controlled　machine　tools.　©　2016,　Editorial　Department　of　CIMS.　All　right　reserved.