The invention discloses an equipment degradation analysis method based on a parameter residual error, and the method comprises the steps of building a neural network model through an improved convolutional neural network algorithm and a large amount of historical data, training an equipment model, and simulating the operation of the equipment; model prediction; and expert rule matching. Through the online comparative analysis of the model calculation value and the real-time monitoring value of the parameters, the residual prediction of the parameters is realized, and the degradation analysis and the fault prediction of the equipment are realized in combination with correlation analysis. According to the method, based on the big data, the improved convolutional neural network algorithm andthe expert rules, in combination with an equipment degradation analysis method, the model prediction accuracy can be improved to 99% or above after learning and perfecting the all-working-condition full-sample data, the prediction alarm time can be brought forward 10-15 days earlier than the fault occurrence time, the gateway moves forwards, the precious time is gained for equipment maintenance, the equipment availability is improved, the safety risk caused by equipment faults is reduced, the maintenance cost is reduced, the non-stop is reduced, and the overall economic benefit is improved.