The application relates to the technical field of manufacturing processes, and specifically discloses a multi-dimensional demand portrait driven adaptive manufacturing process mixed recommendation method and
system, which comprises the following steps: firstly, a multi-dimensional design demand portrait
label system covering basic attributes,
resource constraints, professional requirements and environmental demands is constructed; secondly, the labels are divided into continuous variables and classification variables according to data types, clustering analysis is adopted to complete clustering and output results; thirdly, the
label weight is redistributed by using an
entropy weight method, a design demand
feature vector is constructed, and process data is preprocessed to generate a process
feature vector; fourthly, the two types of feature vectors are hierarchically fused,
label weights are dynamically allocated through an attention mechanism, and an attention weighted
feature vector is obtained; and finally, a mixed recommendation
algorithm is adopted to complete matching, and an optimal process scheme adaptive to the design demand is output; the application effectively improves the accuracy and adaptability of process recommendation, shortens the
process selection cycle, and provides practical protection for enterprises to quickly respond to market demand and consolidate the competitive
advantage in the industry.