With　the　rapid　proliferation　of　electric　vehicles,　the　demand　for　charging　infrastructure　is　surging.　Consequently,　it　becomes　imperative　to　address　the　issue　of　maintenance　for　charging　pile　faults.　To　harness　the　multi-scale　characteristic　present　in　fault　signals　more　effectively,　this　paper　proposed　an　information　fusion　diagnosis　model　based　on　an　improved　multi-scale　convolutional　neural　network　for　the　automated　diagnosis　of　switch　tubes　open-circuit　in　charging　piles.　The　approach　conducts　feature　extraction　at　different　scales　on　the　fusion　information　from　the　original　fault　signals　through　a　parallel　branching　structure.　Furthermore,　a　channel　attention　mechanism　is　incorporated　in　the　final　layer　of　the　network　branch.　This　mechanism　gives　precedence　to　different　channel　features,　dynamically　assigning　weights　to　the　features　obtained　from　distinct　filters.　This　enhances　fault-related　features,　suppresses　ineffectual　ones,　and　subsequently　elevates　the　model＇s　performance.　Ultimately,　the　different　scale　features　acquired　from　each　network　branch　are　merged　and　subjected　to　classification　and　recognition　using　a　Softmax　classifier.　Simulation　results　underscore　the　effectiveness　of　the　proposed　method　in　diagnosing　switch　tube　open-circuit　faults　in　the　charging　pile.　©　2023　IEEE.