Traditional CAD systems have expanded human capabilities in numerical calculations and graphics processing, but they still have the following shortcomings without intelligent support: Failure to provide effective computer support throughout the design process; Inability to handle fuzzy knowledge or design problems described by insufficient conditions; lack of macroscopic knowledge structure analysis, design thinking principles and specific methods and skills to implement design, processing algorithms are mixed with the environment representing the processing object, which makes it difficult to change with the change of the environment or the replacement of processing technology and make necessary modifications and expansions. Based on the idea of intelligent engineering, this article uses simulated annealing method and genetic algorithm to solve the complex clock and watch movement CAD layout problem. Layout problems widely exist in production fields such as machinery manufacturing, clothing, leather, packaging and printing, glass, urban planning, factory layout, etc. The main manifestations are: Cutting one-dimensional bars into pieces of different lengths workpiece to maximize material utilization, such as the problem of cutting steel pipes according to customer orders.

Because of the particularity of the diagnosis task, the software system is required to correctly identify all possible fault types, especially to prevent misdiagnosis or missing diagnosis of abnormal faults. It is also required to evaluate the severity of the fault and predict the deterioration trend to assist users to make timely and correct responses. After the fault diagnosis task is completed, a decision is made as to whether maintenance is required. If maintenance is required, comprehensive analysis and evaluation of feasible maintenance technical schemes are carried out to select the most satisfactory maintenance schemes.

Data mining is an evolutionary process, and data and information are also manifestations of knowledge, but people see concepts, rules, patterns, laws and constraints more as knowledge. Based on data mining, an expert system works by reasoning using expert knowledge, and it consists of knowledge acquisition, knowledge base, reasoning machine, and interpretation part. The knowledge base is used to store experts' experience, knowledge and existing knowledge in the field, and whether it is perfect or not determines the performance of the expert system. Data mining is getting more and more attention, while the existing expert systems or decision support systems still mainly rely on manual collection and manual input of required knowledge and information, which is not only time-consuming and expensive, but also lags behind the time when making time-sensitive decisions and reduces the quality of decision-making.