Both　long-distance　oil　and　gas　pipelines　often　pass　through　areas　with　unstable　geological　conditions　or　natural　disasters.　As　a　result,　they　are　prone　to　bending,　displacement,　and　deformation　due　to　the　action　of　an　external　environmental　loading,　which　poses　a　threat　to　the　safe　operation　of　pipelines.　The　in-line　inspection　method　that　is　based　on　the　implementation　of　high-precision　inertial　measurement　units　(IMU)　has　become　the　main　means　of　pipeline　bending　stress-strain　detection　technique.　However,　to　address　the　problems　of　the　inconsistent　identification,　low　identification　efficiency,　and　high　misjudgment　rate　during　the　application　of　the　traditional　manual　identification　methods,　a　feature　identification　approach　for　the　in-line　inspected　pipeline　bending　strain　based　on　the　employment　of　an　optimized　deep　belief　network　(DBN)　model　is　proposed　in　this　work.　In　addition,　our　model　can　automatically　learn　features　from　the　pipeline　bending　strain　signals　and　complete　classification　and　identification.　On　top　of　that,　after　the　network　model　was　trained　and　tested　by　using　the　actual　pipeline　bending　strain　inspection　data,　the　extracted　results　showed　that　the　model　after　the　implementation　of　the　training　process　could　accurately　identify　and　classify　various　pipeline　features,　with　an　identification　accuracy　and　efficiency　of　97.8%　and　0.02　min/km,　respectively.　The　high　efficiency,　elevated　accuracy,　and　strong　robustness　of　our　method　can　effectively　improve　the　in-line　inspection　procedure　of　pipelines　during　the　enforcement　of　a　bending　strain　load.