Accurately　estimating　the　battery＇s　state　of　health　(SOH)　is　critical　for　battery　efficiency　and　stability.　Despite　significant　progress　in　data-driven　methods,　the　accuracy　of　these　models　is　limited　by　feature　extraction　strategies　and　the　scarcity　of　dataset　samples.　To　address　this　issue,　this　study　develops　a　battery　SOH　estimation　model　tailored　to　the　limited　sample　conditions.　A　refined　composite　multiscale　discrete　sine　entropy　(RCMDSE)　algorithm　is　proposed,　which　combines　composite　multiscale　approaches,　Shannon　entropy　theory,　and　the　discrete　sine　transform.　This　algorithm　is　designed　to　extract　high-quality　battery　entropy　domain　health　features　(HFs)　from　current　and　voltage　signals　at　various　scales　and　levels.　Subsequently,　we　introduce　semi-supervised　learning　concepts　to　enhance　the　estimation　performance　of　the　nu-support　vector　regression　(NuSVR)　algorithm　in　limited　sample　conditions.　The　golden　jackal　optimization　algorithm　(GJO)　is　used　to　improve　the　estimation　accuracy　of　the　NuSVR　algorithm　in　a　semi-supervised　framework.　Comparative　and　ablation　experiments　on　four　datasets　validate　that　the　battery　SOH　estimation　model　maintains　RMSE　and　MAPE　values　of　＜1　%,　even　when　trained　with　only　10　%　of　the　data.　Furthermore,　the　proposed　RCMDSE　algorithm　outperforms　and　is　more　robust　in　HF　extraction　than　the　widely　used　incremental　capacity　(IC)　curve　feature　extraction　method.