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Fast and effective forecasting of the new generation of products is key to enhancing the competitiveness of a company in the market. Although the technological evolution laws in the theory of the solution of inventive problems (TRIZ) have been used to predict the potential states of products for innovation, there is a lack of effective methods to select the best technological evolution law consistently with product replacement and update, and acquiring potentially new technologies and solutions, which relies heavily on designers’ experience and makes it impossible for designers to efficiently use the technological evolution laws to stimulate product innovation. Aimed to bridge this gap, this paper proposes an integrated method consisting of three main steps, combining the technological evolution laws with back propagation neural network (BPNN), international patent classification (IPC) knowledge and company’s technological distance. The best technical evolution law is first searched by a BPNN. The functional verbs and effects in the IPC are then extracted and searched for potential technologies in the Spyder-integrated development environment. Finally, the company's technological distance is used to select analogous sources of potential solutions in the patent database. The final innovative design is determined based on the ideality. The proposed method is applied in the development of a steel pipe-cutting machine to verify its feasibility. The proposed method reduces the dependence on designers’ experience and provides a way to access cross-domain technologies, providing a systematic approach for the technological evolution laws to motivate innovative product design.