To monitor the health status of the boiler in real-time and accurately obtain the temperature and stress situation of the boiler pipeline, a fiber optic grating boiler status monitoring technology based on deep learning is proposed. A sensor structure with dual fiber Bragg grating cascaded packaging and its fixing method have been designed to improve the measurement performance of the sensor. A feature fusion parallel transformer regression prediction model was constructed to process the temperature and strain signals of sensors, achieving accurate recognition of the temperature and strain of sensing units. The experimental results show that the sensitivity of the two gratings in the sensor to temperature is 12.31 pm/℃ and 11.63 pm/℃, and the sensitivity to strain is 1.2 pm/με and 0, eliminating the influence of temperature on strain measurement, with temperature compensation effect. By introducing deep learning algorithms, the difficult problem of high-order mixing terms in the sensitivity of fiber Bragg gratings to temperature and strain mixing in high-temperature environments has been solved. The model′s coefficient of determination is greater than 0.9, and the average absolute error and mean square error are 0.23 and 0.31, respectively, effectively improving the sensor′s recognition accuracy for temperature and stress. In summary, this technology has achieved accurate measurement of temperature and strain in high-temperature environments, making up for the shortcomings of traditional measurement methods such as high-temperature failure and single point measurement. It provides an effective solution for real-time monitoring of boiler working health status.