Accurate forecasting of large-scale cloud server workloads is a critical challenge in modern microservice-based data centers, where complex inter-node dependencies, temporal latency effects, and multi-dimensional fluctuation patterns often undermine the effectiveness of traditional prediction models. To address these challenges, we present Collaborative Fluctuation-aware Learning based on Long Short-Term Memory (CFL-LSTM), a unified framework that combines dynamic correlation mining, causal temporal-spatial alignment, and gated attention-based multi-channel LSTM fusion. The dynamic correlation mining module extracts fluctuation-driven features and quantifies inter-node relationships with polarity-aware delay compensation. The temporal-spatial alignment module corrects auxiliary sequence misalignments to preserve causal consistency. The gated attention fusion mechanism adaptively integrates multi-node representations for fine-grained forecasting. Comprehensive experiments conducted on both the Alibaba Cluster Trace and GAIA datasets demonstrate that CFL-LSTM consistently outperforms competitive statistical and deep learning baselines across multiple error metrics. By incorporating attention-guided feature selection and polarity-aware alignment into a scalable end-to-end architecture, CFL-LSTM offers a novel and deployable solution for robust and accurate cloud workload prediction.