Signal　processing　has　entered　the　era　of　big　data,　and　improving　processing　efficiency　becomes　crucial.　Traditional　computing　architectures　face　computational　efficiency　limitations　due　to　the　separation　of　storage　and　computation.　Array　circuits　based　on　multi-conductor　devices　enable　full　hardware　convolutional　neural　networks　(CNNs),　which　hold　great　potential　to　improve　computational　efficiency.　However,　when　processing　large-scale　convolutional　computations,　there　is　still　a　significant　amount　of　device　redundancy,　resulting　in　low　computational　power　consumption　and　high　computational　costs.　Here,　we　innovatively　propose　a　memristor-based　in-situ　convolutional　strategy,　which　uses　the　dynamic　changes　in　the　conductive　wire,　doping　area,　and　polarization　area　of　memristors　as　the　process　of　convolutional　operations,　and　uses　the　time　required　for　conductance　switching　of　a　single　device　as　the　computation　result,　embodying　convolutional　computation　through　the　unique　spiked　digital　signal　of　the　memristor.　Our　strategy　reasonably　encodes　complex　analog　signals　into　simple　digital　signals　through　a　memristor,　completing　the　convolutional　computation　at　the　device　level,　which　is　essential　for　complex　signal　processing　and　computational　efficiency　improvement.　Based　on　the　implementation　of　device-level　convolutional　computing,　we　have　achieved　feature　recognition　and　noise　filtering　for　braille　signals.　We　believe　that　our　successful　implementation　of　convolutional　computing　at　the　device　level　will　promote　the　construction　of　complex　CNNs　with　large-scale　convolutional　computing　capabilities,　bringing　innovation　and　development　to　the　field　of　neuromorphic　computing.