In the context of global transformation and increasing international competition, effective management of scientific knowledge and the search for external technologies are becoming critical determinants of creating knowledge-intensive technological solutions and ensuring strategic competitive advantages. The objective of this study is to develop and validate a set of mathematical models for quantitative assessment of the influence of the intensity and scale of the search for external technologies on the process of innovation generation. The paper proposes and implements the following: a semantic model of text data of interdisciplinary scientific and technical programs; a model of automatic classification of knowledge by the degree of semantic coherence; a model of automatic generation of answers based on an unstructured knowledge base; an adequacy assessment system; semantic models of the processes of searching for external technologies. An efficiency index has been developed that reflects the density of relevant terms in semantically strong clusters relative to weak ones. A modular architecture of the decision support system is proposed that implements the concept of a text automaton. Econometric analysis revealed the dependence of the effectiveness of innovation stimulation channels (especially the expectations channel) on the stability of the economic environment, emphasizing the need for adaptive strategies in the context of a transition economy.