To　achieve　progressive　and　accurate　decision-making　for　long-term　time　series　data　while　meeting　the　needs　of　privacy-friendly　and　early,　this　paper　proposes　a　universal　framework　for　sequential　progressive　decision-making　(SPD).　This　framework　first　segments　the　data　and　sets　up　multiple　columns　of　neural　networks　according　to　the　number　of　segments.　Each　column　can　make　segmented　decisions　based　on　the　inputs　for　the　period.　Additionally,　without　sharing　the　original　data,　the　framework　leverages　lateral　hidden　layer　connections　between　preceding　and　succeeding　columns　to　obtain　useful　features　for　subsequent　decision-making,　gradually　improving　accuracy　while　avoiding　the　risk　of　data　leakage.　SPD　has　the　advantages　of　privacy　friendliness,　column　model　diversification,　prior　knowledge　reuse,　and　easy　scalability,　making　it　an　effective　framework　for　continuous　decision-making.　The　effectiveness　of　that　was　validated　using　various　network　models　in　handwritten　digit　recognition　and　electrocardiogram　classification　tasks.　The　obtained　experimental　results　reveal　that　SPD　not　only　enables　early　decision-making　while　ensuring　accuracy　but　also　achieves　accuracy　levels　comparable　to　or　even　surpassing　those　obtained　using　complete　data,　with　the　added　benefit　of　privacy　protection.　©　2023　IEEE.