Spatio-temporal　prediction　aims　to　forecast　location　or　time-related　changes　in　the　physical　world　with　disciplinary　knowledge　and　historical　data.　Ensemble　learning　strategies　that　integrate　multiple　base　learners　can　leverage　the　advantages　of　different　models　and　thus　gain　wide　attention　in　the　field　of　spatio-temporal　prediction.　However,　existing　methods　often　ignore　the　unified　expression　of　spatial　and　temporal　heterogeneity　in　the　ensemble　process　and　fail　to　clarify　the　mechanisms　of　model　integration　under　these　constraints,　limiting　the　predictive　ability　and　the　interpretability　of　ensemble　models.　Therefore,　this　study　proposed　a　Matrix-Informed　Ensemble　Learning　Method　(MI-EL)　for　interpretable　spatio-temporal　prediction.　The　core　idea　of　this　method　is　to　decompose　the　spatio-temporal　heterogeneous　ensemble　weight　matrix　into　the　multiplication　of　the　spatial　and　temporal　factor　matrix.　By　constructing　a　spatio-temporal　embedding　learning　module,　it　utilizes　spatial　associations　and　temporal　attributes　of　the　samples　to　solve　the　spatial　and　temporal　factor　matrices,　thereby　achieving　a　unified　expression　of　spatial　and　temporal　heterogeneity　in　the　ensemble　process.　On　this　basis,　interpretable　spatial　and　temporal　score　vectors　are　constructed　for　explicitly　expressing　the　influence　intensities　and　response　rules　of　different　base　learners　under　conditions　of　spatial　and　temporal　heterogeneity.　Experiments　on　traffic　flow,　traffic　speed　and　air　quality　prediction　tasks　show　that　the　proposed　method　outperforms　eight　existing　ensemble　methods　in　the　prediction　accuracies　at　different　time　steps.　Additionally,　it　effectively　identifies　the　performance　patterns　of　base　learners　at　different　spatio-temporal　units　by　assigning　higher　spatiotemporal　scores　to　better-performing　base　learners,　thereby　achieving　superior　prediction　results.