Infrared dim target has small imaging area，less available features and target detection is highly susceptible to backgroundclutter.Therefore，howtodetectdim targetaccuratelyincomplex scenesbecomesatechnicaldifficulty. Inthis paper，basedonthepowerfulfeatureextractionabilityofconvolutionalneuralnetworks，asingleframeinfraredsmalltarget extractionnetworks isdesigned based onfully convolutional neuralnetworks to extractthesmall targetposition accurately. Based on the sequence correlation，the energy accumulation in the space-time domain enhances the intensity of the dim target. Filter out the isolated noise through the adaptive filter and finally infrared dim target is extracted accurately. Experimentalresultsshowthatcomparedwithtraditionalalgorithms，thisalgorithmhasthehighestsignal-to-noiseratioand signal-to-noise ratio gain，which is higher than 1 than the current practical Tophat algorithm，which proves that the proposedalgorithmhashigherextractionaccuracyandlowerfalsealarmrate.