A method of identifying by spectrometry unknown microorganisms from among a set of reference species, comprising: ▪ a first phase of supervised learning of a reference species classification model, comprising: ∘ for each species, acquiring a set of training spectra of identified microorganisms belonging to said species; ∘ transforming each acquired training spectrum into a set of training data according to a predetermined format for their use by a multi-class support vector machine type algorithm; and ∘ determining the classification model of the reference species as a function of the sets of training data by means of said algorithm of multi-class support vector machine type, ▪ a second step of predicting an unknown microorganism to be identified, comprising: ∘ acquiring a spectrum of the unknown microorganism; and ∘ applying a prediction model according to said spectrum and to the classification model to infer at least one type of microorganism to which the unknown microorganism belongs, characterized in that: ▪ the transforming of each acquired training spectrum comprises: ∘ transforming the spectrum into a data vector representative of a structure of the training spectrum; ∘ generating the set of data according to the predetermined format by calculating the tensor product of the data vector by a predetermined vector bijectively representing the position of the reference species of the microorganism in a tree-like hierarchical representation of the reference species in terms of evolution and/or of clinical phenotype; ▪ and the classification model is a classification model of classes corresponding to nodes of the tree of the hierarchical representation, the algorithm of multi-class support vector machine type comprising determining parameters of the classification model by solving a single problem of optimization of a criterion expressed according to the parameters of the classification model under margin constraints comprising so-called "loss functions" quantifying a proximity between the tree nodes. ▪ a first phase of supervised learning of a reference species classification model, comprising: ∘ for each species, acquiring a set of training spectra of identified microorganisms belonging to said species; ∘ transforming each acquired training spectrum into a set of training data according to a predetermined format for their use by a multi-class support vector machine type algorithm; and ∘ determining the classification model of the reference species as a function of the sets of training data by means of said algorithm of multi-class support vector machine type, ∘ for each species, acquiring a set of training spectra of identified microorganisms belonging to said species; ∘ transforming each acquired training spectrum into a set of training data according to a predetermined format for their use by a multi-class support vector machine type algorithm; and ∘ determining the classification model of the reference species as a function of the sets of training data by means of said algorithm of multi-class support vector machine type, ▪ a second step of predicting an unknown microorganism to be identified, comprising: ∘ acquiring a spectrum of the unknown microorganism; and ∘ applying a prediction model according to said spectrum and to the classification model to infer at least one type of microorganism to which the unknown microorganism belongs, ∘ acquiring a spectrum of the unknown microorganism; and ∘ applying a prediction model according to said spectrum and to the classification model to infer at least one type of microorganism to which the unknown microorganism belongs, ▪ the transforming of each acquired training spectrum comprises: ∘ transforming the spectrum into a data vector representative of a structure of the training spectrum; ∘ generating the set of data according to the predetermined format by calculating the tensor product of the data vector by a predetermined vector bijectively representing the position of the reference species of the microorganism in a tree-like hierarchical representation of the reference species in terms of evolution and/or of clinical phenotype; ∘ transforming the spectrum into a data vector representative of a structure of the training spectrum; ∘ generating the set of data according to the predetermined format by calculating the tensor product of the data vector by a predetermined vector bijectively representing the position of the reference species of the microorganism in a tree-like hierarchical representation of the reference species in terms of evolution and/or of clinical phenotype; ▪ and the classification model is a classification model of classes corresponding to nodes of the tree of the hierarchical representation, the algorithm of multi-class support vector machine type comprising determining parameters of the classification model by solving a single problem of optimization of a criterion expressed according to the parameters of the classification model under margin constraints comprising so-called "loss functions" quantifying a proximity between the tree nodes.