In　response　to　the　need　for　multiple　complete　bearing　degradation　datasets　in　traditional　deep　learning　networks　to　predict　the　impact　on　individual　bearings,　a　novel　deep　learning-based　rolling　bearing　remaining　life　prediction　method　is　proposed　in　the　absence　of　fully　degraded　bearng　data.　This　method　involves　processing　the　raw　vibration　data　through　Channel-wise　Attention　Encoder　(CAE)　from　the　Encoder-Channel　Attention　(ECA),　extracting　features　related　to　mutual　correlation　and　relevance,　selecting　the　desired　characteristics,　and　incorporating　the　selected　features　into　the　constructed　Autoformer-based　time　prediction　model　to　forecast　the　degradation　trend　of　bearings＇　remaining　time.　The　feature　extraction　method　proposed　in　this　approach　outperforms　CAE　and　multilayer　perceptual-Attention　Encoder　in　terms　of　feature　extraction　capabilities,　resulting　in　reductions　of　0.0059　and　0.0402　in　mean　square　error,　respectively.　Additionally,　the　indirect　prediction　approach　for　the　degradation　trend　of　the　target　bearing　demonstrates　higher　accuracy　compared　to　Informer　and　Transformer　models,　with　mean　square　error　reductions　of　0.3352　and　0.1174,　respectively.　This　suggests　that　the　combined　deep　learning　model　proposed　in　this　paper　for　predicting　rolling　bearing　life　may　be　a　more　effective　life　prediction　method　deserving　further　research　and　application.