Industrial　two-dimensional　(2D)　matrix　symbols　are　ubiquitous　throughout　the　automatic　assembly　lines.　Most　industrial　2D　symbols　are　corrupted　by　various　inevitable　artifacts.　State-of-the-art　decoding　algorithms　are　not　able　to　directly　handle　low-quality　symbols　irrespective　of　problematic　artifacts.　Degraded　symbols　require　appropriate　preprocessing　methods,　such　as　morphology　filtering,　median　filtering,　or　sharpening　filtering,　according　to　specific　distortion　type.　In　this　paper,　we　first　establish　a　database　including　3000　industrial　2D　symbols　which　are　degraded　by　6　types　of　distortions.　Second,　we　utilize　a　shallow　convolutional　neural　network　(CNN)　to　identify　the　distortion　type　and　estimate　the　quality　grade　for　2D　symbols.　Finally,　we　recommend　an　appropriate　preprocessing　method　for　low-quality　symbol　according　to　its　distortion　type　and　quality　grade.　Experimental　results　indicate　that　the　proposed　method　outperforms　state-of-the-art　methods　in　terms　of　PLCC,　SRCC　and　RMSE.　It　also　promotes　decoding　efficiency　at　the　cost　of　low　extra　time　spent.