With　the　advancement　of　mobile　communication　technology,　there　has　been　a　marked　increase　in　the　demand　for　personalized　and　ubiquitous　Internet-of-Things　(IoT)　services,　raising　the　expectations　for　network　quality　of　service　(QoS)　and　quality　of　experience　(QoE).　Existing　popularity　prediction-based　content　caching　policies　improve　QoS　and　QoE　by　pre-caching　contents　at　the　network　edge,　but　jointly　optimizing　multiple　network　metrics　remains　a　challenge.　To　address　this　challenge,　we　propose　a　many-objective　optimization-based　popularity　prediction　for　cooperative　caching　(MaOPPC-Caching)　framework　for　cloud-edge-end　collaborative　IoT　networks.　This　framework　simultaneously　optimizes　prediction　accuracy,　delay,　offloaded　traffic,　and　load　balance.　We　integrate　three　prediction　algorithms　to　forecast　content　popularity　and　present　a　horizontal　and　vertical　collaborative　caching　decision　strategy　to　generate　caching　forms　based　on　the　predicted　results.　Then,　the　many-objective　evolutionary　algorithm　(MaOEA)　is　employed　to　optimize　the　combined　proportions　to　take　full　advantage　of　hidden　preferences　and　popularity　characteristics　of　both　users　and　items.　To　promote　the　convergence　of　the　framework,　we　present　a　knowledge　mining-based　MaOEA　(KMaOEA)　to　incorporate　knowledge　mining　into　the　optimization　process.　Simulation　results　show　that　the　proposed　MaOPPC-Caching　framework　outperforms　existing　prediction　algorithms　in　terms　of　four　evaluation　indicators.　Furthermore,　KMaOEA　shows　a　significant　advantage　over　NSGA-III　in　load　balance,　as　indicated　by　a　Mann-Whitney　rank　sum　test　with　a　p-value　of　0.040.　IEEE