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Most of the research on identifying and classifying signal models is limited to single signals such as AM, QAM or PSK, and there is a lack of research on the parameters of the identified signals. To make up for this deficiency, an OA (Optimality Confidence and Amplitude Ratio factor) algorithm model is proposed to identify and classify 13 kinds of modulation signals, including 2 kinds of analogue modulation signals (AM and FM), 11 kinds of signal digital modulation (M-SK and M-QAM). Based on the characteristics of the constellation diagram, Confidence and Amplitude Ratio factors are constructed to identify and classify signals with different parameters such as 'Format", "Centre Frequency" and "Sample Rate' and different modulation types. In addition, because of the mis-judgement and confusion problems in identifying classification signals, a three-layer optimisation algorithm is proposed to optimise the OA algorithm model. Numerical simulation results show that the comprehensive identification efficiency of the proposed optimisation algorithm is increased by 18.84%. [ABSTRACT FROM AUTHOR]