To extract the features of a one-dimensional scatter plot, you can try the following methods:

Statistical characteristics: For a one-dimensional scatter plot, you can calculate statistical characteristics such as mean, variance, standard deviation, skewness, and kurtosis. These characteristics can well reflect the distribution and morphological characteristics of data.

Fourier Transform characteristics: If your data is periodic, you can use the Fourier transform to convert it into frequency-domain data and extract its frequency-domain characteristics. For example, you can calculate the peak, center frequency, bandwidth, and other characteristics of the spectrum.

Wavelet transform features: For aperiodic data, you can use wavelet transform to convert it into time-frequency domain data and extract its time-frequency domain features. For example, you can calculate features such as the energy, entropy, average value, and so on of a wavelet packet.

For raw spectral data, you can consider the following processing methods to facilitate feature extraction:

Remove baseline drift: Since spectral data is often affected by baseline drift, you can baseline correct the data to facilitate more accurate extraction of its features.

Data standardization: In order to eliminate dimensional influence between data, you can standardize data, such as normalization according to its mean and standard deviation.

Spectral resampling: If the resolution of your spectral data is too high, you may consider resampling it in order to reduce the dimension of the data and reduce the complexity of feature extraction and classification.

Spectral smoothing: In order to reduce the impact of data noise on feature extraction, you can smooth the spectral data, such as using average filtering or median filtering.

Finally, a classifier can be trained using machine learning algorithms to easily distinguish between different solutions. You can use the extracted features as input to the classifier, and train and test with some samples of known classifications. Commonly used classifiers include support vector machine, random forest, neural network and so on.