Capacitors are widely used in electronic systems and have a key function in EMC (electromagnetic compatibility) compliance. However, the ageing of capacitors results in an alteration of their parameters, which could pose a threat on the normal operation of systems as well as their EMC compliance. Normally, accelerated ageing is employed to shorten the experiment time. After the ageing, the capacitance and equivalent series resistance (ESR) are measured to evaluate the ageing process. In this paper, a new continuous characterization measurement setup is implemented in which the accelerated ageing of the capacitors under test (CUTs) is continuously monitored during the overall accelerated ageing process. It significantly improves the continuity of the measurement and eliminates the errors attributed to the interrupting of the ageing process. This method is validated by comparing measurement results from the new measurement method with results of the conventional method. This was done by subjecting two types of film capacitors to thermal and electrical stress in order to evaluate the accelerated ageing effects. Furthermore, a conditional deep neural network with dropout technique is proposed to predict the accelerated ageing conditions of the capacitors. Instead of only forecasting the failure threshold, the proposed network is able to dynamically predict the accelerated ageing conditions at different elevated temperatures and voltages. This leads to a serious reduction of the total measurement time: from 1000h to 200h.