Extreme-scale　computing　involves　hundreds　of　millions　of　threads　with　multi-level　parallelism　running　on　large-scale　hierarchical　and　heterogeneous　hardware.　Some　OpenMP　multi-threaded　applications　increasingly　suffer　from　runtime　hidden　behaviors　owning　to　shared　resource　contention　as　well　as　software-and　hardware-related　problems.　Such　hidden　behaviors　can　result　in　failure　and　inefficiencies　and　are　among　the　main　challenges　in　system　resiliency.　To　minimize　the　impact　of　hidden　behaviors,　one　must　quickly　and　accurately　detect　and　diagnose　the　hidden　behaviors　that　cause　the　failures.　However,　it　is　difficult　to　identify　hidden　behaviors　in　the　dynamic　and　noisy　data　collected　by　OpenMP　multi-threaded　monitoring　infrastructures.　This　paper　presents　an　anthropomorphic　diagnosis　framework　for　hidden　behaviors　of　OpenMP　multi-threaded　applications.　In　the　framework,　we　first　design　injected　heartbeat　functions　for　OpenMP　multi-threaded　applications.　Then,　we　leverage　the　heartbeat　sequences　to　extract　features　of　hidden　behaviors.　Finally,　we　develop　a　feature　learning-based　algorithm　using　heartbeat　analysis,　namely　HSA,　to　diagnose　hidden　behaviors.　To　evaluate　our　framework,　the　NAS　Parallel　NPB　benchmark,　EPCC　OpenMP　micro-benchmark　suite,　and　Jacobi　benchmark　are　used　to　test　the　performance　of　our　proposed　framework.　The　experimental　results　demonstrate　that　our　framework　successfully　identifies　90.3%　of　the　injected　hidden　behaviors　of　OpenMP　multi-threaded　applications　while　acquiring　low　overhead.(c)　2023　Elsevier　Inc.　All　rights　reserved.