This invention provides a method for estimating the runtime of computing cluster jobs, comprising: extracting multiple features of the job to be predicted to form a multi-dimensional
feature vector and performing normalization
processing; calculating adaptive weights for each dimension of features to generate a weighted feature representation, and generating spatiotemporal embedded features through a gating
fusion mechanism; decoupling the spatiotemporal embedded features to generate a decoupled feature representation; aligning the decoupled feature representations of the current job with those of historical jobs and calculating their similarity, and compensating for environmental disturbances in the similarity; filtering a set of candidate historical jobs based on the comprehensive similarity, and generating a final runtime estimate through weighted
estimation and multi-level correction. This invention achieves accurate representation of job information and high-quality matching of candidate historical jobs through the synergistic effect of multi-dimensional
feature fusion and adaptive weight allocation, feature decoupling and alignment, and similarity environmental disturbance compensation, thus improving the overall accuracy and robustness of the prediction.