The　monitoring　and　analysis　of　massive　data　from　radio　spectrum　monitoring　are　essential　components　of　radio　regulation　work.　To　address　this,　the　paper　proposes　a　lossless　compression　method　based　on　wavelet-like　transform　for　radio　spectrum　monitoring　data.　This　method　first　converts　the　one-dimensional　spectrum　signal　into　a　two-dimensional　matrix　based　on　temporal　correlation.　Once　transformed　into　a　two-dimensional　matrix,　there　is　redundancy　in　both　the　horizontal　and　vertical　directions.　The　algorithm　employs　a　convolutional　neural　network　to　replace　the　prediction　and　update　modules　in　traditional　wavelet　transform,　and　introduces　an　adaptive　compression　block　to　handle　features　of　different　dimensions,　thereby　obtaining　a　more　compact　representation　of　spectrum　data.　Furthermore,　the　paper　designs　a　context-based　deep　entropy　model,　which　utilizes　the　wavelet-like　transform′　s　different　subband　coefficients　to　obtain　entropy　coding　parameters,　estimating　cumulative　probabilities　to　achieve　spectrum　data　compression.　Experimental　results　indicate　that　the　proposed　algorithm　achieves　additional　performance　improvements　compared　to　existing　traditional　lossless　compression　methods　for　spectrum　data,　such　as　Deflate.　Moreover,　when　compared　with　typical　two-dimensional　image　lossless　compression　methods　like　JPEG2000,　PNG,　and　JPEG-LS,　the　proposed　method　achieves　over　20%　better　compression　effectiveness.　©　2024　Editorial　Office　of　EMI　Journal.　All　rights　reserved.