Automatic Modulation Classification (AMC) is a rapidly evolving technology, which can be employed in software defined radio structures, especially in 5G and 6G technology. Machine Learning (ML) can provide novel and efficient technology for modulation classification, especially for systems working in low signal to noise ratio (SNR). In this article, two dynamic systems not reliant on received signal phase lock and frequency lock are presented, with both employing ML to classify the modulation types for different received SNR. The first model is developed from the previous existing literatures, which utilises constellation images (CI) and image classification technology. Here, modulation types can be detected in a dynamic way without phase lock and frequency lock. In the second model, a new method named Graphic Representation of Features (GRF) is proposed, which represents the statistical features as a spider graph for ML. The concepts are tested and verified using simulations and RF data using a lab software defined radio (SDR). The results from the two models are compared. With the GRF techniques an overall classification accuracy of 59% is observed for 0 dB SNR and 86% at 10 dB SNR, compared to a random guess accuracy of 25%
