The　high　energy　density　of　solar　energy　gives　wireless　sensor　networks　advantages　in　outdoor　monitoring　applications.　However,　long-term　stable　monitoring　is　challenging　due　to　frequent　weather　changes,　shading　by　buildings　and　trees,　etc.　The　existing　research　usually　uses　two　technologies　to　solve　the　above　problems:　(1)　the　energy　prediction　algorithm,　and　(2)　the　energy-aware　routing　strategy.　However,　in　an　actual　deployment,　frequent　weather　changes　can　significantly　reduce　the　accuracy　of　the　existing　prediction　algorithms.　When　using　the　algorithms　as　the　support　for　energy-aware　routing,　the　network　lifetime　is　less　than　ideal.　The　existing　routing　strategies　are　in　need　of　further　improvement.　Because　of　its　lack　of　environmental　adaptability,　nodes　consume　energy　quickly　and　have　a　high　mortality　rate.　Therefore,　aiming　at　the　long-term　stability　of　solar　wireless　sensor　networks,　this　paper　proposes　a　prediction　algorithm　based　on　classification　and　recurrent　neural　networks,　and　integrates　the　shadow　judgement　method　from　our　previous　research　to　correct　the　predicted　values.　Furthermore,　we　propose　a　routing　optimization　model　that　can　flexibly　adjust　the　target　according　to　the　solar　intensity.　The　experimental　results　show　that　the　prediction　and　routing　scheduling　algorithm　can　significantly　improve　the　energy　prediction　accuracy　(30–50%)　and　prolong　the　network　lifetime　(10–42%)　in　outdoor　small　sensor　scenarios.　©　The　British　Computer　Society　2023.