The invention discloses an unsupervised multi-
modal equipment instance alignment method based on double-space embedding, and the method comprises the steps: employing a pseudo seed generation module, calculating the similarity of visual and text
modal features, automatically constructing a preliminary pre-alignment instance pair, continuously expanding a high-confidence seed set in an
iteration process, and carrying out the alignment of an unsupervised multi-
modal equipment instance. The method comprises the following steps: firstly, carrying out
data labeling on equipment instances, so as to effectively reduce the dependence on manual
data labeling, then respectively carrying out embedding learning in Euclidean space and hyperbolic space by adopting a double-space embedding strategy, capturing a
local structure relationship between the equipment instances, and carrying out accurate modeling on a hierarchical structure and
global distribution characteristics of the equipment instances, and then, by constructing an adaptive
feature fusion module, carrying out dynamic weighted integration on the embedded representations of the two spaces, and by introducing an iterative constraint mechanism and utilizing a
loss function, ensuring that the representations of the same instances in the double spaces are as close as possible and the distance between different instances keeps enough discrimination.